by Bajpai Labs · Computational biology
From target to ranked candidates in 2–4 weeks.
HelixForge replaces $2–5M screening campaigns with a quantum optimized AI pipeline that surfaces your best 50–100 molecules before a single assay runs.
Delivery pipeline
- Virtual Screening01GNN scoring across ChEMBL, PubChem & your compound library
- Candidate Refinement02Docking · MD simulation · ADME & toxicity scoring
- Ranked Delivery03Top 50–100 molecules · ΔG report · validation playbook
- 80–90%
- Cost savings vs. HTS
- 3–6×
- Faster time-to-lead
- 2–4 wks
- Typical delivery
Traditional screening is slow, expensive, and 90% of candidates fail anyway.
High-throughput wet-lab screening of millions of compounds requires massive capital and infrastructure.
Lengthy validation cycles delay every downstream decision and push programs past funding milestones.
Most candidates fail in vitro. Brute-force screening tests far more compounds than necessary.
An end-to-end AI pipeline that ranks your candidates before any wet-lab work begins.
- 01
Upload your target
Provide a protein structure (PDB), genetic objective, or sequence brief.
- 02
AI pipeline runs
Graph neural networks, molecular docking, MD simulations, and protein language models.
- 03
Ranked candidates delivered
Top 50–500 molecules with predicted efficacy, ADME, and validation playbook.
Built for decisions, not dashboards.
Every architectural choice in HelixForge serves one goal: get your team to the right wet-lab experiments faster.
Most vendors optimize for throughput. HelixForge optimizes for decision quality: surfacing the 50 to 100 molecules worth testing, not millions of maybes.
Predictions are recalibrated against in vitro outcomes, not just public benchmark leaderboards.
Research-driven methods from Bajpai Labs, delivered on pharma timelines with full technical documentation.
You work with the architects who built the pipeline, not a sales team relaying requirements to a black box.
The science behind the pipeline.
Published, peer-reviewed methods composed with proprietary scoring from Bajpai Labs.
Generative models for small-molecule and antibody libraries, trained on ChEMBL, PubChem, and proprietary validation sets.
AutoDock Vina, DiffDock, and custom scoring functions for binding affinity and selectivity prediction.
GROMACS and OpenMM simulations for binding free-energy refinement and conformational analysis.
ESM-2, ESMFold, and AlphaFold integration for structure prediction, function annotation, and variant ranking.
Multi-objective ranking across efficacy, permeability, metabolic stability, and developability metrics.
Codon optimization, off-target prediction, and manufacturability scoring for gene therapy payloads.
Methodology
Closed-Loop Optimization
Each cycle narrows the candidate space using simulation results, not a one-pass screen.
Experimental outcomes from your lab retrain the scoring models, tightening predictions over time.
Models like Insilico Medicine and Exscientia validate: iterative loops cut false positive rates cycle over cycle.
Research rigor. Commercial speed.
HelixForge combines research-driven engineering from Bajpai Labs with computational biology expertise that most pure-software vendors lack.
- Research-driven delivery
Published research, open-source tooling, and production accountability. Not benchmark demos.
- AI + biosciences leadership
Cross-disciplinary expertise spanning ML infrastructure, optimization, and applied biosciences.
- Production accountability
Ranked candidates with full technical documentation your team can take straight into validation.
- Direct line to leadership
You work with the architects who built the pipeline, not a sales team.
Discovery services
Four pipelines. One operating system.
From target discovery through candidate delivery, each service is built for a specific modality, sharing the same production-grade AI infrastructure and the same senior team.
All services- 00
Target Discovery & Disease Mapping
Multi-omics · biology layerStart hereMulti-omics dataset analysis, disease pathway modeling, protein target prioritization, and druggability scoring. Delivers a ranked list of validated biological targets before any chemistry begins.
Engagements typically $100K–$300K per programSee capabilities - 01
Small Molecule Discovery
GNN + dockingGraph neural networks + docking to surface top 50–100 inhibitors against your target.
Engagements typically $150K–$500K per programSee capabilities - 02
Gene Therapy & Sequence Design
Codon + off-targetCodon optimization, off-target prediction, and manufacturability scoring for DNA/RNA payloads.
Engagements typically $200K–$500K per programSee capabilities - 03
Antibody & Protein Engineering
Million-variant libraryMillion-variant libraries ranked by binding, stability, and developability.
Engagements typically $250K–$600K per programSee capabilities
by Bajpai Labs
Ready to test only your best candidates?
30-minute intro call with the Bajpai Labs team. No pitch deck, just a scoping conversation.