Scientific Tools
Vicena gives the research agent access to 27 specialized tools built on 28 scientific libraries, databases, and models spanning literature search, protocol work, chemistry, workspace files, and cloud computing.
The agent selects tools for each step, reports assumptions and gaps, and keeps the tool outputs available for review. You can also pin specific tools or let the agent decide. In the app, the catalog is grouped into expandable concept sections and lives alongside Files and Computer/JupyterLab as switchable right-panel tabs. Try them free.
Looking for chemistry specifically? See our dedicated page on AI for real chemistry — protocol audit, reaction prediction, and quantum chemistry with PySCF.
Designing an experiment before lab work? See Protocol Design for source-backed drafts, proof obligations, and audit certificates.
Need durable project memory? See Workspace Files for saved protocols, audits, uploaded papers, plots, CSVs, and reusable notes.
Need notebooks for modeling, calculations, or simulations? See Scientific Simulations for Vicena's persistent cloud JupyterLab and Science Computer feature.
We are constantly working to improve and expand Vicena for your research and engineering work. Tell us what you need.
Workspace
1 toolsPersistent files and folders for saved research documents, uploads, generated outputs, and reusable scientific work.
Workspace Files
Persistent research file system
Create, read, edit, organize, and search the persistent files shown in the Vicena Files sidebar. Use this for durable research documents, saved protocol audits, uploaded PDFs/images/CSVs, markdown notes, generated plots, and project folders. Do not use this for temporary Jupyter or Linux computer files; use Compute Shell for quick execution work and Workspace Files for files you want to keep.
How it works
The agent treats Workspace Files like a small private research drive. It resolves or creates normal forward-slash paths, reads existing files when needed, writes durable Markdown or data files, updates the Files sidebar, and returns clickable workspace links. When a task needs computation, the agent should use the Science Computer separately and must not claim files moved between the two systems; a direct bridge is not available yet.
Workspace File Flow
The agent uses Workspace Files as the durable source of truth for user-facing documents. It can start from an empty workspace, create folders, save research outputs, or edit existing files without involving the compute sandbox.
Empty workspace
Start pathUseful when the user has no files yet and wants Vicena to create the first project folder or document.
- 1 Create a project folder Choose a clear path such as /Projects/Battery Literature or /Notes.
- 2 Write the first file Create a README, literature-review note, checklist, protocol draft, or audit certificate.
- 3 Return a workspace link Show the saved file path so the user can open it in the right panel.
Existing workspace
Maintenance pathUsed when the user already has uploaded files, saved audits, or project folders.
- 1 Search or list files Find candidate files by path, name, or folder before reading or editing.
- 2 Read before editing Inspect the current file content before patching targeted sections.
- 3 Update only the requested file Keep edits scoped and preserve unrelated workspace files.
Computer boundary
Separate volumeUsed when a task involves both durable Workspace files and temporary Science Computer execution files.
- 1 Use the right volume Workspace Files are durable documents; Computer files are for execution and notebooks.
- 2 Do not claim transfer Direct Workspace-to-Computer file transfer is not available yet; the agent should say that clearly instead of implying a copy happened.
Durable result
The user gets a saved file, an updated folder, a rendered preview, or a clear report that no matching file was found.
Input
Natural language instructions such as “make a project folder,” “save this as /Notes/summary.md,” “do a literature review and write it to a file,” “read the PDF I uploaded,” or “move this audit into the project folder.”
Output
Directory listings, file contents, rendered previews, saved documents, or confirmations with clickable workspace paths that open in the right panel.
Limitations
Workspace Files are separate from the Science Computer/Jupyter filesystem. It does not run code by itself and it does not automatically reorganize or delete files without a clear request. Direct copying, importing, exporting, or moving files between Workspace Files and the Science Computer is not available yet. Guest and Free accounts have 0 GB storage; Plus has 2 GB and Ultra has 10 GB.
Tips
Use this whenever the result should survive beyond the chat: saved notes, literature summaries, protocol drafts, audit certificates, uploaded files, and final plots. Use Compute Shell or Jupyter only for execution, analysis, and temporary computer files.
Example prompts
- › Create /Projects/Graphene Membranes/literature-review.md with a short literature review on graphene oxide membranes for water purification
- › Make a /Notes folder and write a starter README explaining how I should organize my research files
- › Save a protocol audit checklist to /Protocol Audits/checklist.md
Research & Discovery
4 toolsSearch scientific literature, patents, and the web. The AI agent iterates with refined queries across multiple databases until it has comprehensive, cited results.
Literature Review
Indexed paper review with freshness supplement
Searches Vicena’s broad scientific paper index across major journals, preprints, biomedical records, abstracts, and available full text. It extracts compact evidence packets with stable paper/excerpt pointers. When the question needs newer publications than the index may contain, it explicitly announces a scientific-only freshness check before using Web Research.
How it works
The tool extracts constraints from the request, generates targeted queries over the indexed scientific corpus, selects relevant papers, and returns compact evidence packets labeled P1, P2 and P1-E1 style excerpt pointers. For current, recent, state-of-the-art, or empty-index questions, it shows a Freshness Check explaining why an extra pass is needed, then adds a Web Research supplement restricted to scientific literature sources.
Literature Review Flow
2 modesThe tool first searches the indexed paper corpus. When freshness is needed, it explicitly explains the reason, then fills current-literature gaps with a scientific-only web supplement.
Indexed paper pass
Fast pathUsed for the primary literature review over Vicena’s indexed scientific corpus.
- 1 Generate queries Create targeted scientific keyword searches for the indexed corpus.
- 2 Select papers Rank index hits against the user request and constraints before returning evidence.
- 3 Extract pointers Return compact paper packets with P1/P2 labels and P1-E1 excerpt pointers.
- 4 Evidence boundary Report selected paper count, evidence pointer count, freshness status, and answering rules.
Scientific freshness supplement
Contract pathUsed for current, recent, state-of-the-art, or empty-index requests.
- 1 Explain freshness check Tell the user why an extra pass is running: the request is current/recent/state-of-the-art or the index returned no papers.
- 2 Scientific source gate Restrict Web Research to papers, preprints, publisher pages, PubMed/PMC/arXiv/Crossref-style records, and academic pages.
- 3 Reject general web Exclude news, marketing, vendor, consulting, patent analytics, product pages, and general blogs.
- 4 Separate evidence Treat supplement evidence as current-literature coverage distinct from indexed paper evidence.
Grounded synthesis
The chat agent answers from paper evidence pointers and accepted scientific supplement evidence only.
Input
A natural language research question or topic. Be as specific as you would in a database search. You can include constraints like date ranges, organisms, or techniques.
Output
A bounded literature context with selected papers, source URLs, compact excerpts, evidence pointers, a paper evidence boundary, and a visible Freshness Check when a scientific web supplement is needed.
Limitations
The local paper index may lag very recent papers. The automatic supplement is limited to scientific literature sources; paywalled full text and newly posted papers may still be unavailable. Date filters are honored only when the request explicitly asks for a date range or recent/current literature; inferred filters are reported and ignored.
Tips
Use this for broad research questions and trend analysis. For operational protocol drafts, use Protocol Builder instead. For patents, use Patent Research.
Example prompts
- › Find recent papers on CRISPR delivery mechanisms in solid tumors
- › What is the state of the art in perovskite solar cell stability?
- › Review the literature on metal-organic frameworks for CO2 capture
Patent Research
Indexed patent search with freshness supplement
Searches Vicena’s indexed patent corpus for patent records, assignees, inventors, publication dates, and legal technical context. When the question needs newer patent activity than the index may contain, it announces a patent-only freshness check before using Web Research.
How it works
The tool extracts constraints from the request, generates targeted queries over the indexed patent corpus, applies date and inventor filters when explicitly requested, requires indexed assignee/applicant/owner evidence for assignee-specific requests, selects relevant patent records, and returns compact evidence packets labeled with source-stable publication-number pointers such as US10932928B2-E1. For current, recent, state-of-the-art, empty-index, or unverifiable-assignee patent requests, it shows a Freshness Check and adds a Web Research supplement restricted to patent sources.
Patent Research Flow
2 modesThe tool first searches the indexed patent corpus. When freshness is needed, it explains the reason, then fills current patent-record gaps with a patent-only web supplement.
Indexed patent pass
Fast pathUsed for the primary patent search over Vicena’s indexed patent corpus.
- 1 Generate queries Create targeted patent keyword searches for the indexed corpus.
- 2 Apply filters Honor explicit publication-date and inventor filters, require matching assignee metadata for assignee-specific requests, and report ignored inferred date filters.
- 3 Extract pointers Return compact patent packets with source-stable publication-number labels and excerpt pointers such as US10932928B2-E1.
- 4 Evidence boundary Report selected patent count, evidence pointer count, freshness status, and answering rules.
Patent freshness supplement
Contract pathUsed for current, recent, state-of-the-art, or empty-index patent requests.
- 1 Explain freshness check Tell the user why an extra pass is running: the request is current/recent/state-of-the-art or the index returned no patents.
- 2 Patent source gate Restrict Web Research to patent-office records, Google Patents-style records, patent families, and assignee patent pages.
- 3 Reject general web Exclude scientific papers, blogs, news, marketing, vendor pages, consulting pages, product pages, and general web pages.
- 4 Separate evidence Treat supplement evidence as current patent-record coverage distinct from indexed patent evidence.
Grounded synthesis
The chat agent answers from patent evidence pointers and accepted patent supplement evidence only.
Input
A natural language description of the invention or technology. You can include specific assignees, date ranges, or patent classification codes.
Output
A bounded patent context with selected patent records, source URLs, compact excerpts, source-stable evidence pointers, date/inventor/assignee filter reporting, a patent evidence boundary, and a visible Freshness Check when a patent-only web supplement is needed.
Limitations
The local patent index may lag recently published applications and may not expose assignee metadata for every indexed patent. Assignee-specific answers require positive assignee evidence from indexed metadata or the patent-only supplement. The automatic supplement is limited to patent sources such as patent-office records, Google Patents-style pages, patent families, and assignee patent pages. It does not provide legal opinions on patentability or freedom to operate.
Tips
Use this for patent landscape analysis and patent-record discovery. For scientific papers, use Literature Review. For broad non-patent prior art, use Web Research.
Example prompts
- › Find patents on electrochemical CO2 reduction catalysts filed after 2020
- › Search patents for a biodegradable polymer stent with drug elution
- › What patents does BASF hold on lithium-sulfur battery electrolytes?
Protocol Builder
Evidence-grounded operational protocol drafts
Turns a scientific objective into a structured lab protocol draft: checklist, reagents, equipment, chronological steps, data analysis, troubleshooting, assumptions, gaps, and an AI-led sanity review. Helper chemistry and thermo tools provide context when the draft has enough structured inputs.
How it works
Searches indexed papers and scientific web evidence, drafts a protocol, records source-backed details separately from assumptions, and runs a sanity review. Existing chemistry helpers are used as advisory context, not automatic verdicts; the AI decides whether a tool result applies and preserves unresolved concerns.
Protocol Builder Flow
3 modesThe tool turns a scientific objective into a Nature/protocols.io-style operational draft. It separates source-backed details from assumptions and then runs an AI-led sanity review before presenting the final protocol.
Build draft protocol
Fast pathUsed to assemble the first structured protocol from the evidence and explicit assumptions.
- 1 Create protocol sections Produce objective, background, scope, safety, materials, equipment, before-you-begin notes, procedure, timing, QC, expected results, data analysis, troubleshooting, and evidence.
- 2 Label assumptions Record inferred values as assumptions instead of presenting them as extracted facts.
- 3 Track missing details Keep unresolved variables visible when the evidence does not support a concrete choice.
- 4 Reduce critical gaps Run one bounded V1 gap search for the most important missing detail, then preserve anything still unresolved.
Sanity review path
Contract pathUsed to check whether the draft is internally coherent before the final answer is shown.
- 1 Extract check targets Identify explicit solvent/temperature, reagent-vessel, reagent-reagent, yield, stoichiometry, and biological constraint targets when the draft provides enough structure.
- 2 Add helper context Call chemistry or thermo helpers only when required inputs are present; otherwise record the missing input as a gap.
- 3 AI scientific review Use evidence, assumptions, and helper context to flag blockers, warnings, gaps, or notes.
- 4 Preserve concerns Surface unresolved issues in the final sanity review instead of letting the draft hide them.
Operational draft for review
The chat agent presents a scientist-facing protocol in the format: Brief, Materials, Equipment, Safety, Procedure, Timing, QC, Expected Results, Troubleshooting, and Evidence.
Input
A scientific objective or experimental goal. The more specific you are about the target material, organism, technique, scale, constraints, and available equipment, the better the draft.
Output
An operational protocol draft with checklist, reagents, equipment, steps, analysis, troubleshooting, source-backed details, assumptions, missing critical details, and sanity findings labeled as blockers, warnings, gaps, or notes.
Limitations
This is a draft builder, not a validated executable protocol. Helper checks have limited coverage and may miss context. A scientist must review unresolved gaps, assumptions, safety, and feasibility before any lab use.
Tips
Use this when you need to build, draft, create, write, design, or assemble a first operational protocol draft. Use Protocol Audit only after a protocol exists.
Example prompts
- › Build a protocol for synthesizing gold nanoparticles via citrate reduction
- › Build a Western blot protocol for detecting phosphorylated ERK
- › Draft an operational Suzuki coupling protocol for aryl bromides
Web Research
Verified web resources and sourced facts
Searches the open web for practical lab information: technical specifications, manuals, chemical pricing, supplier pages, safety data sheets, and other sources outside papers and patents. It verifies source text before the agent answers.
How it works
Routes each request to the smallest workflow that can answer it. Resource lookups build explicit required resources, verify candidate URLs against source text, and return only verified resources. Comparisons, pricing, specifications, and broader questions use a deeper evidence contract that tracks coverage and missing fields.
Adaptive Web Research Flow
6 modesThe tool first decides whether the user needs verified resource URLs or researched facts, then runs only the path needed for that request.
Resource lookup path
Fast pathUsed for manuals, SDS documents, datasheets, and other requests where the answer should be verified URLs.
- 1 Required resources Create one required resource per requested vendor or entity, such as Sigma-Aldrich SDS for DMSO, CAS 67-68-5.
- 2 Search candidates Run targeted queries and read likely product, SDS, PDF, or manual pages.
- 3 Verify identity Accept a candidate only when source text supports the requested resource, source, subject, and CAS or model identity when requested.
- 4 Lookup boundary Report Required resources, Verified resources, Missing resources, and Lookup complete: Yes or No.
- 5 Verified URLs only The agent may list only verified resource URLs. Related but unverified pages remain discovery metadata.
Evidence research path
Contract pathUsed when the answer needs facts, tables, ranges, comparisons, or coverage across several entities and fields.
- 1 Research contract Define coverage targets, minimum evidence, comparison fields, and quality preferences.
- 2 Coverage-aware search Search and select useful sources across targets before spending reads on duplicates.
- 3 Extract evidence Read candidates and return exact paragraph IDs, source URLs, and supporting text.
- 4 Evidence boundary Report covered targets, missing targets, supported evidence, and whether another pass is needed.
Grounded answer
The chat agent answers from accepted evidence only. If required resources or coverage are missing, it runs another focused pass instead of filling gaps.
Input
Any question about equipment, pricing, suppliers, technical specifications, or practical lab information that is not covered by scientific papers or patents.
Output
Verified resource links for lookup requests, or a structured answer with extracted facts, prices, or specifications. Every claim is grounded in accepted source text and cited by URL.
Limitations
Results depend on what is publicly available on the web. Paywalled content, internal company documents, and very recent pages may not be accessible.
Tips
Use this for practical lab questions: equipment specs, reagent pricing, supplier comparisons, SDS sheets. For scientific literature, use Literature Review.
Example prompts
- › What is the price range of 99.9% pure titanium dioxide from lab suppliers?
- › Find the operating manual for a Bruker 400 MHz NMR spectrometer
- › Compare specifications of benchtop centrifuges rated for 15,000 RPM
Lab Simulation & Validation
5 toolsSimulate your protocol in a virtual lab before going to the bench. The AI validates each step against thermodynamic, kinetic, and chemical constraints.
Protocol Audit
Safety, yield, and feasibility check
Reviews an already described synthesis protocol or explicit procedure-safety question and flags physical impossibilities, proof gaps, and safety risks. It does not build or draft new protocols; use Protocol Builder for that.
How it works
The agent decomposes your protocol and runs deterministic checks on each step: yield validation (reactant MW vs product MW via RDKit), atom balance (conservation of mass from SMILES), chemical compatibility (curated rules + SMARTS-based classification, e.g. HF+glass, alkali+protic, chlorinated+polycarbonate), and boiling point lookup from the Thermo library. Safety data is pulled live from PubChem. Every finding cites its source. Tools return facts, not verdicts — the LLM synthesizes the final judgment.
Input
An existing protocol, procedure excerpt, or concrete safety/feasibility question with the relevant reagents, quantities, temperatures, vessels, and claimed yields where available.
Output
A report flagging impossibilities (yield > 100%, atoms from nowhere), incompatibilities (with cited rules), GHS hazards, and coverage gaps where the audit could not verify and you must apply your own judgment.
Limitations
~20 hardcoded compatibility rules (more coming via RHEACT/CAMEO). Thermo library covers ~70,000 compounds; unusual or proprietary molecules may hit coverage gaps. The audit complements your expertise, it does not replace it.
Tips
Use Protocol Builder to draft a protocol, then audit it here for physical issues before refining the procedure.
Example prompts
- › Check if 6.2g of naproxen methyl ester from 5g naproxen is physically possible
- › Is it safe to reflux ethyl acetate with NaOH in a glass flask at 90°C?
- › Audit this Fischer esterification: refluxing 30 mL acetic acid with 50 mL ethanol and 2 mL conc. H2SO4 in a round-bottom flask at 85°C for 2 hours, expecting 40 g of ethyl acetate
Reaction Energetics
Virtual calorimeter and thermal safety
Calculates reaction enthalpy and the resulting adiabatic temperature rise. Detects thermal runaway risks, solvent boiling hazards, and exothermic safety concerns before you run the reaction.
How it works
Computes reaction enthalpy from standard formation enthalpies (Thermo database, 70,000+ compounds). Calculates adiabatic temperature rise using heat capacities of the reaction mixture. Compares the predicted temperature against solvent boiling points and known decomposition thresholds to flag runaway or boiling risks.
Input
A chemical reaction with reagent names or formulas. Optionally include quantities and solvent for adiabatic temperature rise calculations.
Output
Reaction enthalpy (kJ/mol), adiabatic temperature rise, and safety flags (exothermic warning, boiling risk, runaway risk).
Limitations
Relies on standard formation enthalpy data. Compounds not in the Thermo database cannot be analyzed. Does not model heat dissipation or cooling.
Tips
Use this before running any exothermic reaction. If the adiabatic temperature rise exceeds the solvent boiling point, you need active cooling or slower addition.
Example prompts
- › Is the neutralization of concentrated H2SO4 with NaOH safe in a 500 mL flask?
- › Calculate the adiabatic temperature rise for nitration of toluene
- › Will my Grignard reaction overheat if I add the reagent too fast?
Reaction Kinetics
Rate constants, half-lives, and timing
Estimates how long a reaction takes and how temperature affects speed. Uses the Arrhenius equation and integrated rate laws to calculate rate constants, half-lives, and time-to-completion.
How it works
Uses the Arrhenius equation (k = A * exp(-Ea/RT)) to compute or adjust rate constants at different temperatures. Applies integrated rate laws for zero, first, and second-order reactions to calculate half-lives and time to reach a target conversion.
Input
Activation energy (Ea), pre-exponential factor (A), reaction order, and temperature. Or provide a known rate at one temperature to predict rates at another.
Output
Rate constant at the specified temperature, half-life, and estimated time to reach a target conversion percentage.
Limitations
Requires known kinetic parameters (Ea and A). For reactions where these are unknown, the tool can estimate from two data points at different temperatures.
Tips
Use this to plan reaction timing and temperature optimization. Combine with Reaction Energetics to check both speed and safety at your chosen temperature.
Example prompts
- › How long does a first-order reaction with Ea=85 kJ/mol take at 60C vs 80C?
- › Estimate the half-life of aspirin hydrolysis at room temperature
- › If my reaction is 50% complete in 2 hours at 25C, how fast is it at 40C?
Solubility Predictor
Solvent compatibility and dissolution
Predicts whether a compound dissolves in a given solvent using polarity matching via LogP. Ranks common lab solvents by compatibility. Use before choosing a reaction or workup solvent.
How it works
Calculates the octanol-water partition coefficient (LogP) of the solute using RDKit descriptors. Compares it against the polarity profile of common lab solvents (water, ethanol, DCM, hexane, DMSO, etc.) to rank solvents by predicted compatibility. Retrieves additional solubility data from PubChem when available.
Input
A compound name, SMILES, or CAS number. Optionally specify a solvent to check, or ask for a ranked list of common solvents.
Output
A compatibility prediction (soluble/insoluble/partial) with a ranked list of solvents from best to worst match.
Limitations
Based on LogP polarity matching, which is a heuristic. Does not account for specific solute-solvent interactions, pH effects, or temperature dependence.
Tips
Use this before choosing a reaction solvent or planning a liquid-liquid extraction. For safety data on solvents, combine with the Safety Summary tool.
Example prompts
- › Will ibuprofen dissolve in water or do I need an organic solvent?
- › Rank solvents for dissolving polyethylene glycol 6000
- › Is caffeine more soluble in ethanol or dichloromethane?
Spectroscopy Predictor
Predicted IR, NMR, and MS fingerprints
Predicts the expected spectral fingerprint (IR, NMR, MS) of a compound by identifying its functional groups. Verify whether your reaction produced the correct product or determine what peaks to look for.
How it works
Parses the molecular structure with RDKit and scans it against a library of SMARTS patterns to identify functional groups. Maps each group to its characteristic spectral signatures: IR absorption bands (cm-1), expected 1H/13C NMR chemical shift ranges, and mass fragmentation patterns. Returns a predicted fingerprint you can compare against your experimental spectrum.
Input
A compound name, SMILES string, or CAS number. You can also describe a reaction product and ask what peaks to expect.
Output
Predicted IR bands (cm-1 with assignment), expected NMR chemical shifts (ppm ranges per proton environment), and major mass spec fragments (m/z).
Limitations
Predictions are based on functional group identification, not full quantum mechanical calculations. Fine structure splitting in NMR and exact fragmentation patterns may differ from experiment.
Tips
Use this to verify reaction products: compare the predicted fingerprint against your measured spectrum. For full computational spectra, use the Science Computer with PySCF.
Example prompts
- › What IR peaks should I expect from the product of Fischer esterification?
- › Predict the NMR spectrum of aspirin
- › I see a peak at 1720 cm-1 in my IR spectrum. What functional group is that?
Computational Chemistry
13 toolsAnalyze molecular structures, predict reactions, calculate properties, and assess safety. Powered by RDKit, PubChem, and neural reaction models.
Chemistry Calculator
Quantitative chemistry calculations
Deterministic calculator for stoichiometry, yields, solution preparation, pH, simple equilibria, vapor pressure, and Ideal Gas Law pressure. It returns the quantitative result, assumptions, and model boundary the agent uses when a chemistry question needs numbers and units.
How it works
The calculator keeps the agent-facing request compact, then converts it into mode-specific internal requests before execution. It preserves valid formulas, resolves common names only when unambiguous, tracks excess reagents separately from measured amounts, validates required fields, and rejects ambiguous or under-specified systems instead of guessing. ChemPy handles equation balancing and stoichiometric math; Thermo provides property correlations for supported pure-substance vapor-pressure calculations.
Chemistry Calculator Flow
6 modesThe tool turns a natural-language chemistry question into a validated calculation plan, runs deterministic calculators, and returns model assumptions and boundaries so the agent can interpret the result without inventing extra chemistry.
Stoichiometry engine
StoichiometryUsed for balancing equations, limiting-reagent analysis, theoretical yield, and combustion-style product calculations.
- 1 Build reaction model Use validated reactants and products; infer only narrowly supported textbook products such as complete combustion products when the request clearly specifies that reaction class.
- 2 Balance with ChemPy Solve the symbolic equation balance. If balancing fails, return a validation error rather than assuming a 1:1 reaction.
- 3 Find limiting reagent Convert provided masses to moles, mark excess reagents as non-limiting, and identify the reagent that constrains reaction extent.
- 4 Calculate target amount Return requested product yields or required reactant amounts with moles, grams, the balanced equation, and gas-generation boundary notes when relevant.
Physical chemistry engines
Phys chemUsed for vapor pressure, ideal-gas pressure, solution preparation, dilution, pH, and simple equilibrium questions.
- 1 Choose calculation mode Dispatch to vapor_pressure, pressure_volume, solution_prep, pH, or equilibrium based on the typed validated request.
- 2 Resolve constants Use property correlations or user-supplied constants when the model is in scope; reject systems where the needed constant or species identity is missing.
- 3 Solve with units Compute the numeric result in chemistry-native units such as grams, moles, molarity, atm, psi, mmHg, kPa, or pH.
- 4 Report boundaries State assumptions such as pure-substance vapor-liquid equilibrium or ideal-gas behavior, and distinguish those from sealed-vessel safety or protocol feasibility.
Agent interpretation boundary
BoundaryThe calculator provides quantitative evidence; the final agent may add qualitative scientific interpretation but must not invent additional quantitative or safety conclusions.
- 1 Allowed interpretation The agent can explain reaction class, excess-reagent assumptions, theoretical-vs-isolated yield, equilibrium partial pressure, exothermic reaction class, and why gas generation may matter.
- 2 Needs another tool Heat released, adiabatic temperature rise, closed-vessel pressure beyond supplied ideal-gas inputs, flammability risk, reaction rate, toxicity, compatibility, and protocol feasibility require a dedicated Vicena tool.
- 3 Next-step routing If the user asks for one of those follow-up analyses, the agent should call or recommend Reaction Energetics, Reaction Kinetics, Safety Summary, Protocol Audit, or a related tool with the missing inputs.
Calculation plus bounded interpretation
The chat agent answers from the calculator result, adds supported qualitative context, and routes unsupported quantitative or safety claims to the right tool instead of treating calculator output as a full simulation.
Input
A quantitative chemistry question, optional reagent amounts, product species, solution targets, equilibrium constants, or temperature and gas conditions.
Output
Balanced equation, limiting reagent, reaction extent, theoretical yield, required solution mass, pH, equilibrium result, vapor pressure, or ideal-gas pressure with units, assumptions, and validation errors for corrected follow-up calls.
Limitations
Requires calculation-safe species identities and known constants for equilibrium systems. It does not estimate heat release, adiabatic temperature rise, vessel compatibility, reaction timing, toxicity, protocol feasibility, or real experimental yield; those require dedicated tools or additional inputs.
Tips
Use this for routine quantitative chemistry. For thermal safety use Reaction Energetics, for timing use Reaction Kinetics, and for protocol or hazard conclusions use the relevant audit or safety tool.
Example prompts
- › Balance the combustion of propane and calculate the yield from 50g
- › What is the vapor pressure of ethanol at 60C?
- › How many grams of NaCl do I get from 10g NaOH and excess HCl?
Chemical Intelligence
Compound identity, properties, hazards, and fingerprints
Builds a deterministic compound fact pack from PubChem, RDKit, Thermo, and SMARTS patterns. It resolves one compound, computes descriptors, checks functional groups, summarizes available GHS hazards, estimates solvent compatibility, predicts analytical fingerprints, and compares molecular similarity when references are provided.
How it works
The MCP tool keeps the public interface compact, then normalizes the request into a single compound identity and optional fact sections. It preserves explicit SMILES, rejects ambiguous bare SMILES-like inputs, resolves common names or CAS numbers through PubChem when available, computes local molecular descriptors with RDKit, uses SMARTS for functional groups and spectroscopy hints, asks PubChem for GHS records, uses Thermo for available pure-compound physical properties, and reports data gaps instead of guessing.
Chemical Intelligence Flow
6 modesThe tool resolves one compound, builds deterministic fact sections, and exposes assumptions and data gaps so the chat agent can explain the chemistry without inventing unsupported facts.
Grounded agent interpretation
The agent uses the fact pack as evidence for qualitative explanation, and routes unsupported quantitative safety or protocol claims to dedicated tools.
Input
A compound-level request with a compound name, CAS number, or explicit SMILES. Optional fields can request sections, solvents for focused solubility guidance, or reference compounds for similarity.
Output
A grounded fact pack covering identity, identifiers, RDKit properties, Thermo physical properties when available, functional groups, PubChem/GHS hazards, LogP-based solubility guidance, SMARTS-based spectroscopy hints, similarity scores, assumptions, provenance, and data gaps.
Limitations
This is a fact-pack tool, not a protocol audit, toxicity model, measured solubility database, or full spectral simulator. Hazard records may be missing from PubChem, solubility is estimated from polarity/LogP, and spectra are functional-group hints rather than measured or quantum-calculated spectra.
Tips
Use this before asking the agent to reason about an unfamiliar compound. For numeric lab math use Chemistry Calculator, for reaction heat use Reaction Energetics, and for kinetics use Reaction Kinetics.
Example prompts
- › Build a chemical intelligence fact pack for aspirin
- › Analyze caffeine and show solubility guidance for water and ethanol
- › Compare ibuprofen to naproxen
Reaction Intelligence
Reaction prediction, feasibility, compatibility, energetics, and kinetics
Builds a reaction-level fact pack that interprets a reaction, checks balance and atom conservation, predicts products when SMILES inputs are available, suggests retrosynthesis routes for targets, screens compatibility, and runs energetics or kinetics sections when the required inputs are supplied.
How it works
The MCP tool keeps a compact public schema, normalizes the request into reaction sections, and calls deterministic or model-backed helpers only when the required inputs are present. ChemPy balances formula equations, RDKit checks atom conservation and yield feasibility, SMARTS rules handle simple textbook reactions, ReactionT5v2 handles complex prediction and retrosynthesis, curated compatibility rules screen reagent and vessel pairs, and Thermo/Arrhenius engines are used for thermal or timing sections.
Reaction Intelligence Flow
6 modesThe tool turns a reaction request into bounded evidence sections so the chat agent can interpret the chemistry without treating model suggestions or limited checks as final verdicts.
Input
A reaction-level request with optional reaction equation, reactants, products, target molecule, solvent, vessel, conditions, amounts, and requested sections.
Output
A structured reaction fact pack with interpretation, prediction or route suggestions, balance checks, yield feasibility, compatibility checks, energetics or kinetics output, assumptions, boundaries, data gaps, and provenance.
Limitations
This is not an executable protocol or proof of safety. Neural predictions and retrosynthesis are suggestions; compatibility rules have limited coverage; energetics is screening-level; kinetics requires explicit rate constants or Arrhenius parameters; yield checks only bound physical feasibility.
Tips
Use this for reaction-level grounding before the agent explains feasibility. Use Chemistry Calculator for routine numeric stoichiometry and Protocol Audit for procedure-level review.
Example prompts
- › Build a reaction intelligence fact pack for ethanol plus acetic acid
- › Predict the product of CCO plus CC(=O)O under acid catalysis
- › Check atom balance and compatibility for this reaction in glass
Chemical Analyzer
Properties and hazards lookup
Retrieves physical properties and safety data for chemicals from PubChem. Returns boiling points, molecular weights, GHS hazard classifications, and reactive group information.
How it works
Queries the PubChem REST API (116 million compounds) by name, CAS number, or SMILES. Returns physical constants (melting point, boiling point, density, molecular weight), GHS hazard pictograms, H-statements, and reactive group classifications.
Example prompts
- › What are the physical properties and hazards of dimethyl sulfoxide?
- › Look up the boiling point and GHS classification of acetonitrile
- › Is sodium azide classified as explosive?
Reaction Predictor
Neural network reaction prediction
Predicts products of complex chemical reactions using a trained neural network. Optimized for multi-step and pharmaceutical reactions like Suzuki coupling, Heck reactions, and Sonogashira coupling. ~93% accuracy on complex patent reactions.
How it works
Converts reactants to SMILES notation and feeds them through a Molecular Transformer, a sequence-to-sequence neural network trained on 1.2 million patent reactions. The model treats reactions as translations between molecular languages. Predicted products are validated for chemical validity using RDKit before being returned.
Input
Reactant names, SMILES strings, or a natural language description of the reaction. You can specify catalysts and conditions.
Output
Predicted product(s) as SMILES with names and a confidence indicator. The product is validated for chemical correctness.
Limitations
Optimized for organic synthesis. Less reliable for inorganic, enzymatic, or radical reactions. Accuracy is ~93% on complex patent-style reactions, lower on unusual chemistries.
Tips
Use this for complex, multi-step reactions. For simple textbook reactions (acid-base, esterification), use Simple Reactions instead for higher accuracy.
Example prompts
- › Predict the product of Suzuki coupling between phenylboronic acid and 4-bromoanisole
- › What do I get when I react aniline with acetic anhydride?
- › Predict the product of a Heck reaction with styrene and iodobenzene
Retrosynthesis
Work backwards from target molecule
Suggests reactants needed to synthesize a target molecule. Proposes synthetic routes by working backwards from your desired product, identifying feasible starting materials and reaction conditions.
How it works
Takes a target molecule in SMILES format and runs it through the Molecular Transformer in reverse mode. The model proposes disconnections and suggests commercially available starting materials. Multiple synthetic routes are ranked by feasibility.
Input
A target molecule as a name, SMILES string, or drawn structure.
Output
One or more proposed synthetic routes with starting materials and reaction types. Routes are ranked by feasibility.
Limitations
Works best for drug-like organic molecules. Very large molecules (polymers) or inorganic compounds may produce unreliable suggestions.
Tips
Use this when you know what you want to make but not how to make it. Combine with the Reaction Predictor to validate each proposed step.
Example prompts
- › How can I synthesize ibuprofen from simple starting materials?
- › Suggest a retrosynthetic route to paracetamol
- › What reactants do I need to make 4-nitroaniline?
Simple Reactions
Textbook reaction predictions
Predicts products of common textbook reactions: esterification, acid-base neutralization, SN2 substitution, hydrolysis, and oxidation/reduction of simple substrates.
How it works
Uses rule-based reaction templates implemented in RDKit for well-characterized reaction types. Unlike the neural predictor, this is deterministic and optimized for simple, well-understood transformations where accuracy is near 100%.
Input
Reactant names or formulas and the reaction type (e.g. "esterification", "neutralization", "SN2").
Output
The predicted product with a balanced equation.
Limitations
Only covers well-known textbook reaction types. For complex or novel reactions, use the Reaction Predictor.
Tips
Use this for common organic and inorganic reactions where accuracy matters more than novelty. The rule-based approach is deterministic, so identical inputs always give identical outputs.
Example prompts
- › What is the product of ethanol and acetic acid with an acid catalyst?
- › Predict the product of SN2 reaction between NaBr and 1-chlorobutane
- › What happens when you mix HCl and NaOH?
Functional Group Identifier
SMARTS substructure matching
Identifies functional groups present in a molecule from its SMILES representation. Uses SMARTS pattern matching to scan for common organic functional groups.
How it works
Parses the input molecule with RDKit and runs it against a curated library of SMARTS patterns covering 60+ functional groups: alcohols, amines, carbonyls, halogens, heterocycles, and more. Reports all matches with their positions in the molecule.
Input
A molecule name, SMILES string, or CAS number.
Output
A list of all functional groups found in the molecule, with their names and positions.
Limitations
Identifies standard organic functional groups. Unusual or very complex heterocyclic motifs may not be covered.
Tips
Use this to understand an unfamiliar molecule before running other tools. Knowing the functional groups helps predict reactivity, solubility, and spectral features.
Example prompts
- › What functional groups are present in aspirin?
- › Identify the functional groups in glucose
- › Does this SMILES contain any amine groups? CC(=O)Nc1ccc(O)cc1
Molecular Descriptors
Drug-likeness and Lipinski analysis
Calculates molecular descriptors and checks Lipinski's Rule of Five for drug-likeness. Returns LogP, molecular weight, hydrogen bond donors/acceptors, and polar surface area.
How it works
Computes 2D molecular descriptors using RDKit: LogP (Wildman-Crippen), molecular weight, number of hydrogen bond donors and acceptors, topological polar surface area, and rotatable bond count. Evaluates Lipinski's Rule of Five and Veber's rules to assess oral bioavailability.
Input
A molecule name, SMILES string, or CAS number.
Output
A table of descriptors (LogP, MW, HBD, HBA, TPSA, rotatable bonds) and a pass/fail assessment against Lipinski and Veber rules.
Limitations
Evaluates drug-likeness based on physicochemical properties only. Does not predict biological activity, toxicity, or metabolic stability.
Tips
Use this early in drug design to filter candidates. Molecules that fail Lipinski's rules are unlikely to be orally bioavailable.
Example prompts
- › Does caffeine pass Lipinski's Rule of Five?
- › Calculate the LogP and polar surface area of metformin
- › Is this molecule drug-like? CC(=O)Oc1ccccc1C(=O)O
Molecular Similarity
Tanimoto fingerprint comparison
Calculates structural similarity between two molecules using Morgan fingerprints and Tanimoto coefficient. Returns a score between 0 (completely different) and 1 (identical).
How it works
Generates Morgan circular fingerprints (radius 2, 2048 bits) for each molecule using RDKit. Computes the Tanimoto coefficient (intersection over union of bit vectors) to quantify structural similarity. This is the same method used in pharmaceutical virtual screening.
Input
Two molecule names, SMILES strings, or CAS numbers to compare.
Output
A Tanimoto similarity score between 0.0 (completely different) and 1.0 (identical), with a qualitative assessment.
Limitations
Measures 2D structural similarity only. Molecules with similar shapes but different connectivity (3D similarity) are not captured.
Tips
Scores above 0.85 generally indicate very similar molecules. Use this to find structural analogs or check if two molecules are variants of the same scaffold.
Example prompts
- › How similar are ibuprofen and naproxen?
- › Compare the structures of caffeine and theobromine
- › Is aspirin structurally similar to salicylic acid?
Safety Summary
GHS hazard information
Retrieves GHS safety information from PubChem for any chemical. Returns hazard statements, signal words, and pictogram descriptions. Accepts chemical names, SMILES, or CAS numbers.
How it works
Queries PubChem's GHS classification data. Returns the signal word (Danger/Warning), all H-statements (hazard), P-statements (precaution), and pictogram codes. Accepts input as chemical name, SMILES, or CAS number.
Input
A chemical name, SMILES string, or CAS number.
Output
Signal word (Danger/Warning), GHS pictograms, hazard statements (H-codes), and precautionary statements (P-codes).
Limitations
Returns the GHS classification from PubChem. Compounds not registered in PubChem will not have data. Does not assess mixture hazards.
Tips
Always check safety before handling a new reagent. Combine with the Solubility Predictor to assess both compatibility and safety of your chosen solvents.
Example prompts
- › What are the safety hazards of hydrofluoric acid?
- › Is methanol toxic? What precautions do I need?
- › Give me the GHS classification for sodium cyanide
Molecule Converters
Name, SMILES, and CAS conversion
Converts between molecule representations: common names to SMILES, SMILES to names, and molecules to CAS registry numbers. Uses PubChem as the reference database.
How it works
Resolves chemical identifiers through PubChem's standardization pipeline. Converts between IUPAC names, common names, CAS registry numbers, and SMILES/InChI representations. Handles synonyms and trade names.
Input
A molecule in any format: common name, IUPAC name, CAS number, or SMILES string.
Output
The molecule in the requested format (SMILES, name, or CAS), with the PubChem CID for reference.
Limitations
Relies on PubChem's compound registry. Very new or proprietary compounds may not be found.
Tips
Use this to translate between formats when other tools require SMILES input. Most chemistry tools in Vicena also accept names directly, but SMILES is unambiguous.
Example prompts
- › What is the SMILES for ibuprofen?
- › Convert CAS 50-78-2 to a chemical name
- › What is the CAS number for dimethylformamide?
Quantum Chemistry
DFT, Hartree-Fock, and post-HF calculations
Runs first-principles electronic structure calculations in a visible PySCF notebook. The agent creates the notebook with runnable cells, executes it on your cloud computer, and returns energies, orbitals, and geometries. Supports DFT, Hartree-Fock, and post-HF methods like MP2.
How it works
The agent selects an appropriate method and basis set (e.g. B3LYP/6-31G* for DFT), creates a named notebook with ordered PySCF setup, molecule, run, and results cells, and executes it in your persistent JupyterLab environment. Results are parsed back into the chat with tables and plots.
Input
A molecule (name or SMILES) and the property you want to compute: energy, optimized geometry, HOMO-LUMO gap, IR spectrum, partial charges, etc.
Output
A notebook with the calculation, numerical results, and any plotted data (orbitals, geometries, spectra). Files persist in your cloud environment.
Limitations
CPU-only environment: small-to-medium molecules only (~30 heavy atoms). No periodic DFT or correlated methods beyond MP2. For large systems, use semi-empirical methods instead.
Tips
Combine with Molecular Descriptors for quick ballpark values before running a full quantum calculation. For reaction energetics that don't require first principles, use Reaction Energetics (much faster).
Example prompts
- › Create and run a PySCF notebook named water_hf_sto3g.ipynb that runs RHF/STO-3G for water and prints the orbital energies
- › Create and run a PySCF notebook named ammonia_b3lyp_dipole.ipynb that computes the dipole moment of ammonia with B3LYP/6-31G*
- › Create and run a PySCF notebook named caffeine_dft_gap.ipynb that optimizes caffeine with DFT and reports the HOMO-LUMO gap
Science Computer
4 toolsA persistent cloud computer with JupyterLab where you and the AI agent collaborate in real notebooks. Write code, run simulations, and visualize results together.
Compute Shell
Terminal for packages, quick commands, and inline calculations
Run shell commands in an isolated Linux environment. Use it to install packages, inspect the compute environment, manipulate sandbox files, run quick calculations, and execute small scripts with inline output.
How it works
Each user gets a dedicated Linux container with sandbox storage. The agent runs shell commands directly and reads stdout/stderr. Packages installed via pip or apt persist inside the Science Computer. The container is isolated from other users and separate from Workspace Files.
Input
Natural language instructions for what you want to install, inspect, calculate, or run quickly. You can also paste shell commands directly.
Output
The command output (stdout/stderr) displayed inline in chat. Files created by commands remain in the Science Computer filesystem, not Workspace Files.
Limitations
CPU-only environment (no GPU). Long-running computations may time out after 2 minutes per command. Network access is available for downloading packages and data. Direct transfer between Science Computer files and Workspace Files is not available yet.
Tips
Use this for package installation, quick one-off commands, and short inline computations. For iterative computation with plots and tables, use Jupyter instead.
Example prompts
- › Run Python to generate a sine-wave plot from synthetic data and display the image
- › Use Python to calculate the mean, standard deviation, and linear fit for x=[0,1,2,3,4] and y=[1.0,2.1,4.0,6.2,8.1]
- › Use RDKit to calculate the molecular weight and LogP of caffeine from SMILES Cn1cnc2c1c(=O)n(C)c(=O)n2C
Workspace State
Read-only visible UI state
Read the current known user-facing workspace state without changing anything. It reports which Jupyter notebook the UI is showing, which notebook the notebook tool last touched, and the previous visible notebook when known.
How it works
The tool reads canonical state recorded by the app backend from JupyterLab UI events and notebook-tool actions. It never opens notebooks, switches tabs, lists files, runs code, or mutates workspace state.
Input
Ask about the current visible notebook, previous visible notebook, or ambiguous phrases like current notebook and active tab.
Output
A compact JSON state snapshot with known surfaces such as Jupyter visible_notebook, previous_visible_notebook, and tool_active_notebook.
Limitations
Returns only state the app has observed from the browser and tool calls. Unknown fields mean that surface has not reported state yet.
Tips
Use this before acting on ambiguous UI-relative requests. Use Jupyter for actual notebook edits or execution.
Example prompts
- › Which notebook am I looking at right now?
- › What was the previous visible notebook?
- › Before appending, check the current visible workspace state.
Jupyter
Visible Python notebooks for simulations, analysis, and plots
Work in a shared JupyterLab notebook when the code, plots, and outputs should be visible, editable, and reusable. The agent can create, inspect, edit, and run cells while returning compact transcripts and displayed images.
How it works
Runs a real JupyterLab instance in the Science Computer. The agent uses a small tool family: Jupyter Manager for notebook lifecycle and whole-notebook reads, Jupyter Editor for cell edits, and Jupyter Runner for execution and output inspection. Named notebooks become tool-active for later calls. If the browser reports a newer UI-active notebook, pathless writes fail as ambiguous instead of guessing; choose notebook_path, notebook_target="tool_active", or notebook_target="ui_active".
Notebook Workflow
6 modesUse notebooks for visible computational work. Use Compute Shell only for support tasks such as package installs, file checks, or short one-off commands.
New notebook
create_notebookBest when the user asks for a fresh notebook with a known structure. It never overwrites an existing file.
- 1 Provide cells Pass notebook_path and an ordered cells list. Then run the saved code cells when outputs are needed.
- 2 Return the transcript Report the path, cell counts, source, execution counts, outputs, and any displayed images.
Continue a notebook
active targetBest when the user says continue, append, current notebook, or active notebook.
- 1 Resolve the target Omit notebook_path only when there is no newer UI-active notebook. Otherwise choose tool_active, ui_active, or a named path.
- 2 Append or edit directly Use append_cell for new work, replace_cell for a known bad cell, or edit_cell for a small source change, then run the relevant cell.
Inspect existing work
read actionsBest when the user asks what is in the notebook, what changed, or what the last output shows.
- 1 Read without running Use read_notebook or read_cell. Set include_outputs=true when outputs or images matter.
- 2 Answer from state Use the returned cell source, text output, errors, tables, and displayed images. Do not infer from filenames.
Visible computational record
The user sees a live notebook with cells and outputs inside the Science Computer session folder. Notebook files are separate from Workspace Files.
Input
Requests for visible Python work: create a notebook, build a simulation, analyze data, plot results, append a cell, replace a known cell, run all cells, or inspect the last displayed notebook image.
Output
A live Jupyter notebook plus a concise transcript with the action, path, cell counts, changed or read source, execution counts, text/error output, and image markers. Displayed notebook images are attached to the tool result.
Limitations
CPU-only environment. Very large datasets (>1 GB) and GPU training are not supported. Notebook files live in the Science Computer session folder, not Workspace Files. Direct transfer between notebooks and Workspace Files is not available yet. Destructive changes must be explicit: create_notebook never overwrites, delete_cells requires indices, and replace_notebook requires cells.
Tips
Use this for reusable multi-step computation with plots, tables, and code the user may edit. Use read_notebook or read_cell before modifying user-side edits. Use Compute Shell for package installs, quick file checks, and support commands that should not become notebook cells.
Example prompts
- › [USE JUPYTER] Build a notebook that fits this Michaelis-Menten dataset and reports Km, Vmax, residuals, and a plot: substrate_uM=[5,10,20,50,100,200], rate=[0.08,0.15,0.27,0.50,0.72,0.88].
- › [USE JUPYTER] In a visible notebook, simulate 1D diffusion from a point source, compare two diffusion coefficients, and plot concentration profiles over time.
- › [USE JUPYTER] Create a notebook that analyzes this calibration curve, fits a linear model, reports R^2 and LOD, and plots the fit with residuals: concentration=[0,1,2,5,10], signal=[0.03,0.18,0.35,0.82,1.61].
Computer File System
Temporary sandbox file operations
Read, write, and manage files inside the active compute sandbox or Jupyter-backed working directory. This is separate from the Files sidebar and Workspace Files.
How it works
Provides structured filesystem access within the compute environment. The agent can read, write, move, delete, and search sandbox files when a task explicitly involves a filesystem path, uploaded dataset, notebook output, or project source file.
Input
Natural language instructions for sandbox file operations, or specific computer/Jupyter file paths to read or write.
Output
File contents, directory listings, or confirmation of write/move/delete operations.
Limitations
This is not the Vicena Workspace Files filesystem. Direct copy/import/export/move between the Science Computer and Workspace Files is not available yet. For notes, protocol certificates, saved canvas files, or vague requests like “create a new file,” use the Files workspace instead.
Tips
Use this for data files used by computation. Use workspace files for documents you want to manage from the Vicena Files sidebar.
Example prompts
- › Read the CSV I uploaded and summarize the columns
- › Save this notebook output as /results/summary.json
- › List files in the current Jupyter working directory
Science Stack
28 databases, libraries & modelsThe databases, libraries, and models that power Vicena's tools. All are available in the Scientific Simulations notebook environment for direct use in your notebooks.
Scientific Paper Index
Databaseby Vicena
Vicena’s indexed scientific corpus spanning major journal articles, preprints, biomedical records, abstracts, and available full text. Used for fast evidence retrieval before freshness checks on the open web.
by National Library of Medicine (NIH)
The US National Library of Medicine database with over 36 million biomedical citations. The primary source for life sciences and biomedical literature worldwide.
by Cornell University
Cornell University's open-access repository hosting over 2.4 million preprints in physics, mathematics, computer science, and quantitative biology. Covers cutting-edge research before peer review.
by Google
Google's academic search engine indexing the full text of scholarly literature across publishers, disciplines, and formats. Covers papers, theses, books, and conference proceedings.
by Google
Google's patent search covering over 120 million patent documents from 100+ patent offices worldwide, including the USPTO, EPO, and WIPO.
by National Center for Biotechnology Information (NIH)
The world's largest open chemistry database, maintained by the NIH. Contains data on 116 million compounds including structures, properties, biological activities, safety information, and patent references.
by Greg Landrum and contributors
The industry-standard open-source cheminformatics toolkit used by Pfizer, Novartis, and Merck. Handles molecular representation, substructure search, fingerprinting, and property calculation.
by Bjoern Dahlgren
A Python library for physical chemistry. Solves stoichiometry, balances equations, computes equilibrium constants, and models chemical kinetics from first principles.
by Caleb Bell and contributors
An open-source thermodynamic properties library covering 70,000+ chemicals. Calculates vapor pressure, heat capacity, enthalpy, and phase equilibria using validated correlations from the DIPPR database.
by Qiming Sun et al.
A quantum chemistry package for Hartree-Fock, DFT, and post-Hartree-Fock calculations. Used in academic research for electronic structure simulations of molecules and materials.
by Technical University of Denmark
The Atomic Simulation Environment, a set of tools for setting up, running, and analyzing atomistic simulations. Interfaces with dozens of quantum chemistry and molecular dynamics codes.
by NumPy community
The fundamental package for numerical computing in Python. Provides N-dimensional arrays, linear algebra, Fourier transforms, and random number generators. The foundation of nearly all scientific Python.
by SciPy community
Built on NumPy, SciPy adds optimization, integration, interpolation, signal processing, and statistical functions. The go-to library for scientific and engineering computation.
by Wes McKinney and contributors
The standard library for data manipulation in Python. DataFrames make it easy to clean, transform, and analyze tabular data from experiments, simulations, and databases.
by John D. Hunter and contributors
The most widely used plotting library in science. Produces publication-quality figures, histograms, spectra, and scatter plots. Used in thousands of peer-reviewed papers every year.
by Plotly Technologies Inc.
An interactive visualization library for 3D plots, dashboards, and dynamic charts. Particularly useful for exploring molecular structures, reaction landscapes, and multi-dimensional data.
by Meta AI (FAIR)
Meta's open-source deep learning framework, the most popular in academic research. Powers neural networks for reaction prediction, molecular property estimation, and scientific data analysis.
by Hugging Face
Hugging Face's library providing access to thousands of pre-trained models for NLP, computer vision, and scientific applications. Used for text analysis of papers and chemical language models.
by SymPy community
A symbolic mathematics library for Python. Solves equations algebraically, computes integrals and derivatives, and simplifies expressions. Useful for deriving analytical solutions to scientific problems.
by INRIA and contributors
The most widely used machine learning library in Python. Provides classification, regression, clustering, and dimensionality reduction algorithms for analyzing scientific datasets.
by Intel, Willow Garage, and contributors
The standard computer vision library with tools for image processing, feature detection, and analysis. Used in microscopy, materials characterization, and automated lab image analysis.
by Caleb Bell
A Python library for fluid mechanics calculations. Computes pressure drops, pipe friction factors, and hydraulic properties for chemical engineering and process design.
by Project Jupyter
The open-source interactive development environment used by millions of scientists. Originally developed at UC Berkeley, Jupyter is the standard for reproducible computational research across all scientific disciplines.
Molecular Transformer
Modelby Philippe Schwaller et al. (IBM Research / EPFL)
A sequence-to-sequence neural network that treats chemical reactions as translations between molecular languages (SMILES). Trained on millions of patent reactions, it predicts products with ~93% accuracy on complex organic synthesis.
Morgan fingerprints
Modelby H. L. Morgan (1965), extended by RDKit
A circular fingerprinting algorithm that encodes the local chemical environment around each atom. Widely used in drug discovery for virtual screening and similarity searching because it captures both topology and atom types.
SMARTS
Standardby Daylight Chemical Information Systems
A pattern language for describing molecular substructures. Used to identify functional groups, pharmacophores, and reactive sites by matching atoms and bonds in molecular graphs.
Arrhenius equation
Modelby Svante Arrhenius (1889)
The foundational model in chemical kinetics describing how reaction rate constants depend on temperature. Developed by Svante Arrhenius in 1889, it remains the standard for predicting reaction speed.
Vicena Workspace Files
Standardby Vicena
The durable per-user file system behind Vicena’s Files tab. It stores research documents, uploads, protocol audit certificates, images, PDFs, CSVs, and reusable notes separately from the execution computer.
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