Drug discovery software — built around how your computational and medicinal chemists actually work.
Custom platforms for in-silico screening, ADMET and property prediction, cheminformatics data foundations, knowledge graphs, and AI-assisted analytics — engineered to sit alongside the ELN, LIMS, registration, and chemistry tooling your scientists already rely on.
In-Silico Screening & Virtual Library Workflows
Custom platforms for virtual library design, docking pipelines, similarity and pharmacophore search, and structure-based screening — orchestrated so your computational chemists can move from a query to a ranked, annotated shortlist inside one workspace.
ADMET & Property Prediction Platforms
Software for ADMET, physicochemical, and selectivity prediction — model registries, batch scoring pipelines, and explainability hooks so your chemists see not just a number but the data and model lineage behind it.
Knowledge Graphs Over Your Research Sources
Graphs that connect targets, compounds, assays, scientific literature, and internal experiments — so your scientists can traverse the relationships between programs, projects, and prior work without re-running searches across siloed systems.
Cheminformatics Data Foundation
A research data fabric across your compound registration, assay results, screening data, and structure activity records — with schema-level lineage, controlled chemistry vocabularies, and role-scoped access so AI and analytics workloads run on data with documented provenance.
Lab Data Integration & Workflow Automation
Connectors and workflow automation that sit alongside your ELN, LIMS, registration, and instrument-data systems via documented APIs and standard formats — your IT and integration teams own the connections; we own the discovery-side application logic.
MLOps & Model Governance for Chemistry Models
Model cards, training data fingerprints, evaluation harnesses, drift detection, retraining gates, and change-control logs for every chemistry model — engineering artifacts your QA, IT, and downstream regulatory teams can review as part of their own validation work.
In-Silico Screening, Inside Your IP Boundary
Virtual screening, docking, and similarity workflows orchestrated inside a tenancy your IT and security teams configure — so your chemists can move from query to ranked shortlist without exposing proprietary structures to external models.
Property and ADMET Signals, Earlier in the Workflow
ADMET, selectivity, and physicochemical prediction surfaced beside the chemist’s design view — with model cards, confidence reporting, and lineage on every score, so your team sees the chemistry behind the number, not just the number.
Every discovery stage, one engineering spine.
Target identification through candidate selection — each stage sits on the same software capability rail and shares one IP-protected research data foundation underneath, so your chemists, informatics team, and downstream QA all work from the same engineering core.
Target Identification
Search, evidence aggregation, target dossier
Hit Identification
Virtual screening, similarity, pharmacophore
Hit-to-Lead
Triage, scaffold analysis, SAR review
Lead Optimization
ADMET, selectivity, property prediction
Candidate Selection
Decision package, pre-clinical handoff
In-silico screening & virtual libraries
Docking, similarity, and library workflows orchestrated end-to-end
ADMET & property prediction
Model registries, batch scoring, confidence reporting
Knowledge graph over research sources
Targets, compounds, assays, literature, and internal experiments linked
Chemist-facing decision surface
Workspaces that surface lineage and explainability beside every score
One IP-protected research data foundation, shared across every stage
Compressed Hit-to-Lead Loops for Your Chemists
Workflows that compress the search → screen → score → decide loop — virtual screening, property prediction, literature search, and internal experiments accessible from one workspace with the underlying data lineage preserved.
Better Signals Going Into Pre-Clinical
Property, ADMET, and selectivity signals surfaced earlier in the workflow — supporting your chemists’ design decisions and giving your pre-clinical team a stronger candidate package to build from.
Your Chemistry IP Stays Your Chemistry IP
Compound structures, assay data, registration records, and generative outputs handled inside an IP boundary your IT and security teams define. Generative and ML pipelines are configured so proprietary structures are not silently used to train external models.
No Rip-and-Replace of Your Discovery Stack
We sit alongside the ELN, LIMS, registration, and cheminformatics tools your scientists already rely on — using documented APIs and standard chemistry data formats. Adding discovery software capability without forcing you to displace the validated tooling your bench already trusts.
Transparent ML for Chemistry, Not Another Black Box
Chemistry models ship with model cards, training data lineage, evaluation results, confidence reporting, and change-control history — so your chemists and your QA team can read the model behind the score before any decision is made.
Engineered with IP protection, data discipline, and audit awareness for discovery work
Chemistry IP Inside a Defined Boundary
Compound structures, registration data, assay results, and generative outputs are handled inside an IP boundary your IT and security teams configure. Generative and ML pipelines are designed so proprietary chemistry is not silently used to train external models.
Data Discipline Your Downstream QA Can Read
Schema-level lineage, immutable audit records, controlled chemistry vocabularies, and database-level constraints — applied so research data stays attributable and contemporaneous when your QA, GLP, and IND-enabling teams pick it up later.
Chemistry ML Lifecycle Artifacts Your QA Can Review
Model cards, training data fingerprints, evaluation harnesses, drift monitoring, and change-control logs for every chemistry model — every prediction arrives with a documented lifecycle your chemists and reviewers can read.
Our Implementation Process
Discovery, Workflow Mapping & Engineering Framing
We map your discovery workflows end to end — target identification, hit identification, hit-to-lead, lead optimization, candidate selection — audit data readiness across your chemistry systems, and frame the engineering and integration shape before scoping the build. Scientific direction and program decisions stay with your team.
Architecture, Data Strategy & IP Boundary
Design the platform architecture, cheminformatics data foundation, model lifecycle, IP boundary, and security posture — including audit-trail design, role-scoped access, MLOps practices, and tenancy boundaries — alongside your IT, security, and QA stakeholders.
Build & Iterate
Iterative full-stack development of the discovery platform — data pipelines, model services, screening workflows, knowledge graph, chemist-facing UI, and governance tooling — with engineering artifacts (test coverage, evaluation results, change logs) captured as part of the build.
Integration With Your Discovery Stack & QA Handoff
Connect to your existing ELN, LIMS, registration, and chemistry systems via documented APIs and standard formats, run end-to-end UAT with your discovery informatics and chemistry stakeholders, and assemble the engineering documentation set your QA team needs as inputs into their own validation work.
Deployment, Hypercare & Lifecycle Operations
Phased rollout to your computational and medicinal chemistry teams. An initial hypercare period covers monitoring, model drift response, retraining considerations, and change-control reviews so the platform stays in a known state as your programs evolve.
Frequently Asked Questions
How does drug discovery software fit alongside our existing ELN, LIMS, registration, and cheminformatics stack?
Our platforms are designed to coexist with the ELN, LIMS, registration, and chemistry tooling your scientists already use — connecting via documented APIs and standard chemistry data formats (SDF, MOL, SMILES, InChI, and the integration endpoints those systems expose). Your IT and integration teams own the actual connections into your validated tools; we own the discovery-side application logic, the data fabric beneath it, and the chemist-facing UI on top. We do not claim partnerships, certifications, or pre-built integrations with any third-party ELN, LIMS, or cheminformatics vendor — every connector is engineered to your environment.
How is our proprietary chemistry IP protected when AI and ML are in the loop?
Compound structures, registration data, assay results, and any generative outputs are handled inside an IP boundary your IT and security teams define. Generative and ML pipelines are configured so proprietary chemistry is not silently used to train external models — the boundary, the tenancy, and the data-flow rules are written into the architecture from day one and reviewable by your security function. Training pipelines use scoped, internal datasets; retrieval and inference run inside the tenancy you control. The engineering practices we apply are aligned with what customers in regulated research environments typically expect, but we make no compliance certifications on your behalf.
Can your AI/ML models be trusted for hit-to-lead and lead-optimization decisions?
Every chemistry model we ship arrives with a documented lifecycle — model card, training data lineage, evaluation harness results, confidence reporting, and change-control history. ADMET, selectivity, and property predictions surface alongside the data and model lineage behind them, not as bare scores. The role of the model is to support your chemists’ design decisions, not replace them — confidence thresholds, human-in-the-loop review patterns, and clinician- or chemist-facing explainability hooks are first-class engineering features. Your scientific leadership decides how the outputs are used.
Does pre-clinical discovery software need to be 21 CFR Part 11 or GLP compliant?
Most discovery and early lead optimization work sits before GLP nonclinical safety, so 21 CFR Part 11 and full GxP validation are usually not required for the discovery platform itself. But the data, models, and audit trail you build during discovery eventually flow into GLP nonclinical, IND-enabling, and regulated downstream work — and your QA team will ask to trace it. We engineer the platform with audit-trail logging, lineage, role-scoped access, and lifecycle artifacts your QA team can use as inputs if and when GLP / GxP applicability is established. Validation execution, GLP determination, and any regulatory submission remain with your QA and regulatory functions.
What does a typical drug discovery engagement look like, and how do you scope it?
Engagement scope, timeline, and investment vary by program and are defined during discovery — we do not quote fixed durations or fixed scientific outcomes on a public page. Discovery is where we map your computational and medicinal chemistry workflows end to end, audit data readiness across your ELN / LIMS / registration / chemistry stack, and frame the engineering and integration shape before any production-bound code is written. After discovery, the build is typically phased so the highest-priority capability (for example, an in-silico screening workspace or an ADMET prediction service) goes live first and your team can review the platform before later phases land.
Can you build platforms for ADMET prediction, generative chemistry, or virtual screening specifically?
Yes — each of those is a candidate engagement shape under this practice. ADMET and property-prediction platforms wrap your internal and selected published models with batch scoring, model cards, confidence reporting, and explainability hooks. Generative chemistry tooling is engineered with retrieval over your internal sources, IP-boundary controls, and evaluation harnesses that track factuality and synthetic accessibility over time. Virtual screening platforms orchestrate library design, docking, similarity, and pharmacophore workflows alongside your chemists’ existing tools. We engineer the software; your chemistry and informatics leadership choose which underlying scientific approaches and reference models to bring inside the platform, and your team owns the scientific interpretation of every output.
Building discovery software around your chemistry stack?
Book a free 30-minute discovery call. We will review your computational and medicinal chemistry workflows, talk through the engineering and integration shape against the ELN, LIMS, registration, and chemistry tooling you already run, and outline a realistic scope. Scientific direction and IP governance remain with your team.