Target Identification & Validation
Multi-omics data, disease pathways, and literature combined to prioritise the highest-potential biological targets. AlphaFold-based structure prediction replaces work that once took years.
Drug Discovery AI: Faster Targets, Better Leads.
ML for target identification, virtual screening, and lead optimisation — built for pharma companies, biotech firms, and research institutes.
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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 far larger than any physical screen. Generative chemistry proposes novel, synthesisable structures beyond existing compound libraries.
Lead Optimisation & ADMET Prediction
Multi-parameter models balance potency, selectivity, stability, and permeability simultaneously. ADMET liabilities flagged before synthesis — molecules filtered against the most common causes of late-stage failure.
Where the Traditional Discovery Process Breaks Down
The science is compelling and the target looks valid, yet a programme still burns years and hundreds of millions before a candidate emerges — because the bottlenecks are computational, not biological.
See How We Fix This
What Drug Discovery AI Manages
Five highest-value stages of the discovery pipeline — managed from a single AI platform built for pharmaceutical companies, biotech firms, and research institutes.
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 outcome from machine learning applied to target identification, virtual screening, lead optimisation, or ADMET prediction.
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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 organisation running drug discovery programmes — from large pharma portfolios to early-stage biotech companies, academic drug discovery centres, and Indian generic and specialty pharma.
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Large Pharmaceutical Companies
Large Pharmaceutical Companies
More programmes moving faster with the same scientific headcount — AI-powered target identification and lead optimisation across multiple therapeutic areas and chemistry teams.
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Biotech and Small Pharma
Biotech and Small Pharma
Compress the timeline from target to clinical candidate without a large computational chemistry team — the capability of a much larger organisation, designed for small science teams.
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Academic Drug Discovery Centres
Academic Drug Discovery Centres
AI-powered discovery tools make academic programmes more productive and resulting candidates more attractive to industry partners and investors.
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Generic and Specialty Pharma in India
Generic and Specialty Pharma in India
Drug repurposing and molecule optimisation for tropical diseases, AMR, and NCDs — the same computational capability available to global pharma, built for Indian development teams.
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Research Institutes & Drug Development Teams
Research Institutes & Drug Development Teams
Reduce manual work in literature synthesis, target scoring, and hit prioritisation — freeing scientific talent for the decisions that require human judgment.
Built for the Tools Your Discovery Team Already Works With
Drug Discovery AI integrates with your existing cheminformatics, structural biology, and electronic lab notebook infrastructure — no forced migrations, no parallel systems, no proprietary data required to start.
Structural Biology
Protein Structure & Molecular Modelling
Works with molecular modelling and structure-based drug design environments your team already uses.
Cheminformatics
Cheminformatics & Compound Management
Works alongside existing cheminformatics platforms — no forced replacement.
Bioactivity Data
Public & Proprietary Bioactivity Sources
Pre-trained on public datasets, useful from day one. Fine-tuned on your internal data when available.
Lab Integration
Electronic Lab Notebook & LIMS
Data flows between the platform and your ELN and LIMS — scientists stay in familiar environments.
Compliance
Regulatory & Data Governance
Meets compliance requirements across multiple jurisdictions, including CDSCO for India-based programmes.
Genomics
Multi-Omics & Genomics Platforms
Integrates multi-omics data sources for target identification and disease 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.
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Award-Winning AI Development & Consulting
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Global Spring Winner
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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 — useful from day one. When proprietary assay or compound data is added, models fine-tune to your specific series. More data improves predictions, but it is not required to start.
[ 2 ]
How are AI predictions validated before we commit synthesis resources to them?
Every prediction includes a confidence estimate and supporting data. A subset of candidates is experimentally validated before acting on the full list — building confidence in the model's applicability to your chemical series.
[ 3 ]
Does the platform integrate with our existing cheminformatics and ELN tools?
Yes — Schrödinger, OpenEye, RDKit, Benchling, LabArchives, Dotmatics, and others. Data flows between the platform and your existing tools so scientists work in familiar environments.
[ 4 ]
Can the platform support Indian drug development programmes, including CDSCO requirements?
Yes — including repurposing for tropical diseases, AMR, and NCDs relevant to the Indian market. Compliance documentation is aligned with CDSCO guidelines and the DPDP Act 2023.
[ 5 ]
How does AI-generated drug repurposing analysis work in practice?
Approved drug databases, target networks, and clinical outcomes are searched for existing molecules with potential in new indications. Each candidate comes with supporting evidence, a predicted mechanism, and a data gap analysis — a structured starting point, not a raw list.
[ 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 against retrosynthetic accessibility scores before appearing in hit lists — your team receives candidates predicted to be both potent and practically synthesisable.
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