Target Identification & Validation
Multi-omics data, disease pathways, and literature prioritise the highest-potential targets. AlphaFold structure prediction replaces years of work.
Award-Winning Drug Discovery AI
AI Across Every Stage of Discovery.
Target identification, virtual screening, lead optimisation, ADMET prediction, and drug repurposing — the five stages where machine learning saves the most time and cost.
Virtual Screening & Hit Identification
Deep learning predicts molecule-target interaction across libraries larger than any physical screen. Generative chemistry proposes novel, synthesisable structures.
Lead Optimisation & ADMET Prediction
Models balance potency, selectivity, stability, and permeability at once. ADMET liabilities flagged before synthesis, against the top causes of late-stage failure.
Where Traditional Drug Discovery Breaks Down
A valid target still burns years and hundreds of millions before a candidate emerges — because the real bottlenecks are computational, not biological.
How We Fix ThisWhat Drug Discovery AI Manages
Five discovery pipeline stages on one AI platform.
What Your Discovery Team Works With Every Day
Five purpose-built workspaces for medicinal chemists, computational biologists, programme leaders, and regulatory teams. Outputs designed to inform scientific decisions — not replace scientific judgment.
Target Intelligence Dashboard
Every target scored by evidence strength, druggability, tissue expression, and genetic validation — not just which targets look promising, but why.
Virtual Screening Workspace
Ranked binding predictions, selectivity assessments, and preliminary ADMET profiles. Generative chemistry proposals for novel synthesisable structures — a prioritised shortlist, not a uniform candidate list.
Lead Optimisation Modeller
Evaluate hundreds of virtual analogues before committing synthesis resources. Multi-parameter models balance potency, selectivity, stability, and permeability simultaneously — not one property at a time.
ADMET Prediction Engine
ADMET predictions at every stage from hit to lead. Metabolic liabilities, hERG toxicity, and poor permeability flagged before synthesis — resources redirected to candidates with better profiles.
Repurposing & Evidence Layer
Repurposing candidates surfaced from approved drug databases, target networks, and clinical outcomes — with supporting evidence, predicted mechanism, and data gap analysis.
Drug Discovery AI: What the Numbers Showed.
Each metric ties to a real machine-learning drug discovery outcome.
Book a Live Demo10+ Years
Average time from target to clinical translation using traditional methods. AI compresses the computational stages — the parts bottlenecked by throughput, not biology.
40.9%
Share of AI drug discovery methods using machine learning as the primary technique — the dominant approach across target identification, screening, and optimisation.
Weeks
Instead of years for protein structure prediction with AlphaFold. Programmes now move to structure-based drug design at a pace previously impossible.
10⁶⁰
Synthesisable drug-like molecules in chemical space. Traditional HTS covers a few million. Generative AI expands the search beyond any existing library.
Phase II
Clinical entry reached by an AI-designed molecule for idiopathic pulmonary fibrosis. Active programmes at pharma companies of every size are running AI-designed candidates.
Significant
Cost reduction in early-stage development reported by organisations using AI for lead optimisation. Each design-synthesise-test cycle saved is direct budget recovered.
Who This Platform Serves
Built for every team running drug discovery programmes.
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Large Pharmaceutical Companies
Large Pharmaceutical Companies
More programmes moving faster with the same headcount.
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Biotech and Small Pharma
Biotech and Small Pharma
Target to clinical candidate without a big chemistry team.
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Academic Drug Discovery Centres
Academic Drug Discovery Centres
Discovery tools that attract industry and investor partners.
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Generic and Specialty Pharma in India
Generic and Specialty Pharma in India
Repurposing and molecule optimisation for AMR and NCDs.
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Research Institutes & Drug Development Teams
Research Institutes & Drug Development Teams
Less manual literature synthesis, scoring, and hit triage.
Built for the Tools Your Discovery Team Already Works With
Integrates with your existing cheminformatics, structural biology, and ELN infrastructure. No forced migrations, no proprietary data required.
Structural Biology
Protein Structure & Molecular Modelling
Works with molecular modelling and structure-based drug design tools.
Cheminformatics
Cheminformatics & Compound Management
Works alongside existing cheminformatics platforms, no replacement.
Bioactivity Data
Public & Proprietary Bioactivity Sources
Pre-trained on public datasets, fine-tuned on your internal data.
Lab Integration
Electronic Lab Notebook & LIMS
Data flows between the platform and your ELN and LIMS systems.
Compliance
Regulatory & Data Governance
Meets compliance across jurisdictions, including CDSCO for India.
Genomics
Multi-Omics & Genomics Platforms
Integrates multi-omics data for target ID and pathway analysis.
The Molecule That Changes Everything Is Out There. AI Helps You Find It Faster.
Every year AI saves is a year earlier patients access a new treatment. Book a 30-minute demo and see exactly how it works for your pipeline.
Book a Discovery Demo
Award-Winning AI Development & Consulting
100 Fastest Growth Companies
Global Spring Winner
Top App Development Company
AWS Partner Network
Google Cloud Partner
Highly Rated on Trustpilot
Verified Agency
Top App Development Company
ASSOCHAM Member
Frequently Asked Questions
[ 1 ]
How much proprietary data do we need to use the platform effectively?
Foundation models are pre-trained on ChEMBL, PubChem, UniProt, and the PDB, so they are useful from day one. Proprietary assay data fine-tunes them to your series.
[ 2 ]
How are AI predictions validated before we commit synthesis resources to them?
Every prediction carries a confidence estimate and supporting data. A subset is experimentally validated before you commit to the full list.
[ 3 ]
Does the platform integrate with our existing cheminformatics and ELN tools?
Yes — Schrödinger, OpenEye, RDKit, Benchling, LabArchives, and Dotmatics. Data flows both ways, so scientists stay in familiar tools.
[ 4 ]
Can the platform support Indian drug development programmes, including CDSCO requirements?
Yes — repurposing for tropical diseases, AMR, and NCDs, with documentation aligned to CDSCO guidelines and the DPDP Act 2023.
[ 5 ]
How does AI-generated drug repurposing analysis work in practice?
Approved drug databases and target networks are searched for new indications. Each candidate ships with evidence, mechanism, and data gap analysis.
[ 6 ]
What happens when the AI proposes a molecule that is difficult or expensive to synthesise?
Synthesisability is a built-in constraint — proposals are filtered on retrosynthetic accessibility, so hit lists stay potent and practical.
Global presence
