AI-Driven Target & Drug Discovery Platform

Accelerate target identification, modality-specific design, and hit-to-lead optimization with agentic AI workflows built for biotech R&D teams.

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Platform Overview

A unified ecosystem combining:

1 Target discovery engines

Multi-omics AI + embeddings + KGs for identifying disease-relevant targets.

2 Modality-specific design modules

Small molecules, peptides, proximity inducers.

3 Predictive & generative AI model stack

Structure, function, binding, docking, ADMET, PPI.

4 Dry + wet-lab integration

Active-learning cycles guiding experiments.

Multi-Omics Pipelines

5 AI-native R&D orchestration

Strategy → workflows → models → insights → iteration.

AI Healthcare Diagnostics

1 Target discovery engines

Multi-omics AI + embeddings + KGs for identifying disease-relevant targets.

2 Modality-specific design modules

Small molecules, peptides, proximity inducers.

3 Predictive & generative AI model stack

Structure, function, binding, docking, ADMET, PPI.

4 Dry + wet-lab integration

Active-learning cycles guiding experiments.

Multi-Omics Pipelines

5 AI-native R&D orchestration

Strategy → workflows → models → insights → iteration.

AI Healthcare Diagnostics

CORE MODULES

Small Molecules Platform

What it enables

Covalent & non-covalent inhibitors
GPCRs, kinases, ion channels
Enzymes & nuclear receptors
Allosteric & cryptic pocket targeting
SAR automation and prioritization
Full ADMET property optimization
Selectivity scoring vs isoforms/mutants

AI Capabilities

Docking
Binding site detection
Virtual screening
Selectivity + off-target prediction
Lead optimization scoring

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CORE MODULES

Peptide Platform

What it enables

Linear / cyclic / hybrid peptides
Non-natural amino acids
Phage/RNA display integration
Optimization for permeability & oral availability
Peptide → small-molecule conversion workflows

AI Capabilities

Sequence-to-structure prediction
Activity prediction
Stability modeling
Generative peptide design

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CORE MODULES

Proximity Inducers Platform (PROTACs, glues, disruptors)

What it enables

Ternary complex prediction
Molecular glues & degraders
PPI disruptors
Native + induced PPI targeting
Membrane supramolecular assemblies
Multi-modality workflows

AI Capabilities

PPI interface prediction
Ligand-induced complex assembly
Generative designs for degraders/disruptors

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Discover More Related Case Studies

All Case Studies

Trusted by Industry Leaders:

Level 1 – AI-Orchestrated R&D Strategy

Validated AI-generated drug discovery strategies
Automated project plans & risk evaluation
Expert-guided iterative optimization
Dynamic adaptation during experimental cycles

Level 2 – Drug Discovery Workflows

End-to-end workflows for each modality
Tailored for biological mechanism & therapeutic class
Integrated computational + experimental loop
Workflow components adapt as data grows

Level 3 – Predictive & Generative AI Stack

DockX – AI docking engine (small molecules + peptides)
BindFormer – binding + affinity prediction (all modalities)
PPI-Net – protein-protein interface prediction
ADMET-Graph – 80+ endpoint ADMET predictor
SelectaAI – selectivity & off-target engine

Level 4 – Data Engine

Multi-omics data ingestion (scRNA, bulk RNA, proteomics, metabolomics)
Experimental + public data fusion
Automated feature engineering
Knowledge graph augmentation
Active learning for experiment prioritization

Level 1 – AI-Orchestrated R&D Strategy

Validated AI-generated drug discovery strategies
Automated project plans & risk evaluation
Expert-guided iterative optimization
Dynamic adaptation during experimental cycles

Level 2 – Drug Discovery Workflows

End-to-end workflows for each modality
Tailored for biological mechanism & therapeutic class
Integrated computational + experimental loop
Workflow components adapt as data grows

Level 3 – Predictive & Generative AI Stack

DockX – AI docking engine (small molecules + peptides)
BindFormer – binding + affinity prediction (all modalities)
PPI-Net – protein-protein interface prediction
ADMET-Graph – 80+ endpoint ADMET predictor
SelectaAI – selectivity & off-target engine

Level 4 – Data Engine

Multi-omics data ingestion (scRNA, bulk RNA, proteomics, metabolomics)
Experimental + public data fusion
Automated feature engineering
Knowledge graph augmentation
Active learning for experiment prioritization

Didn’t find exactly what you were looking for?

Tell us what you’re building – we’ll map out the options, evaluate feasibility, and recommend the optimal technical pathway.

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For Whom

For early-stage biotechs

No internal data science team
Need to show investors a clear AI-R&D plan
Lack of validated target discovery workflows

For growth-stage & platform biotechs

Scaling multiple therapeutic programs
Fragmented data across wet/dry labs
Need for unified internal AI platform

For pharma & CRO R&D

Throughput & cost pressure
Reproducibility & regulatory complexity
Slow cycle times between experiments

For early-stage biotechs

No internal data science team
Need to show investors a clear AI-R&D plan
Lack of validated target discovery workflows

For growth-stage & platform biotechs

Scaling multiple therapeutic programs
Fragmented data across wet/dry labs
Need for unified internal AI platform

For pharma & CRO R&D

Throughput & cost pressure
Reproducibility & regulatory complexity
Slow cycle times between experiments

Get in touch with us. 
We’re here to assist you.

Gary Martin

Head of Biotech AI Partnerships

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AI-Driven Target & Drug Discovery Platform

A unified ecosystem combining:

1 Target discovery engines

2 Modality-specific design modules

3 Predictive & generative AI model stack

4 Dry + wet-lab integration

5 AI-native R&D orchestration

1 Target discovery engines

2 Modality-specific design modules

3 Predictive & generative AI model stack

4 Dry + wet-lab integration

5 AI-native R&D orchestration

Small Molecules Platform

What it enables

AI Capabilities

Peptide Platform

What it enables

AI Capabilities

Proximity Inducers Platform (PROTACs, glues, disruptors)

What it enables

AI Capabilities

Discover More Related Case Studies

Level 1 – AI-Orchestrated R&D Strategy

Level 2 – Drug Discovery Workflows

Level 3 – Predictive & Generative AI Stack

Level 4 – Data Engine

Level 1 – AI-Orchestrated R&D Strategy

Level 2 – Drug Discovery Workflows

Level 3 – Predictive & Generative AI Stack

Level 4 – Data Engine

Didn’t find exactly what you were looking for?

For Whom

For early-stage biotechs

No internal data science team

Need to show investors a clear AI-R&D plan

Lack of validated target discovery workflows

For growth-stage & platform biotechs

Scaling multiple therapeutic programs

Fragmented data across wet/dry labs

Need for unified internal AI platform

For pharma & CRO R&D

Throughput & cost pressure

Reproducibility & regulatory complexity

Slow cycle times between experiments

For early-stage biotechs

No internal data science team

Need to show investors a clear AI-R&D plan

Lack of validated target discovery workflows

For growth-stage & platform biotechs

Scaling multiple therapeutic programs

Fragmented data across wet/dry labs

Need for unified internal AI platform

For pharma & CRO R&D

Throughput & cost pressure

Reproducibility & regulatory complexity

Slow cycle times between experiments

Get in touch with us. We’re here to assist you.

Gary Martin

Your AI Idea, Our Technical Precision

Get in touch with us. 
We’re here to assist you.