From molecule analysis to clinical trial intelligence — BioDevAI gives pharmaceutical companies, biotech startups, universities, and research labs a unified AI platform for the entire drug discovery pipeline.
Analysis Tools
Scientific Databases
Research Domains
AI Skills
Traditional drug development takes 10–15 years, costs up to $2.8 billion per drug, and faces 90% clinical failure rates. AI is transforming this reality.
10–15 yrs
Average development timeline
$2.8B
Average cost per approved drug
90%
Clinical trial failure rate
Source: MarketsandMarkets, Grand View Research — AI in Drug Discovery Market 2025–2035 ($19.89B → $160.49B, 23.2% CAGR)
34+ specialized AI analysis tools spanning the full drug discovery pipeline — from hit identification to clinical intelligence.
Analyze molecules, predict drug-like properties, and generate novel compounds with AI-powered computational chemistry tools.
Calculate molecular properties, descriptors, Lipinski compliance, and fingerprints using RDKit
Predict absorption, distribution, metabolism, excretion, and toxicity profiles with DeepChem
Simulate protein-ligand interactions with DiffDock and AlphaFold integration
Generate structurally similar drug candidates with property constraints
Predict physicochemical and bioactivity properties with deep learning models
Identify and validate drug targets across OpenTargets, STRING, and UniProt
Map disease pathways and gene interactions via KEGG and Reactome
Search ChEMBL, PubChem, DrugBank, ZINC, and more in a unified interface
Sequence analysis, gene discovery, variant annotation, and pathway enrichment for genomics-driven drug discovery.
Analyze DNA, RNA, and protein sequences with BioPython
Retrieve gene information from Ensembl and NCBI via gget
Annotate genetic variants with clinical significance from ClinVar and Ensembl
Run GO, KEGG, and Reactome enrichment on gene lists
Analyze protein sequences, domains, and structures from UniProt and AlphaFold
Run sequence similarity searches against reference databases
Analyze scRNA-seq data with Scanpy-powered pipelines for cell type discovery and gene regulatory analysis.
Quality control, normalization, and feature selection for scRNA-seq
Dimensionality reduction (UMAP, t-SNE) and Leiden/Louvain clustering
Identify differentially expressed genes and cell type markers
Automated cell type annotation using CellxGene Census references
Pseudotime ordering and lineage inference with scVI
Infer GRNs from single-cell data with Arboreto
Search clinical trials, FDA approvals, adverse events, and pharmacogenomics data for translational research.
Search ClinicalTrials.gov for trials by condition, drug, sponsor, phase, and status
Query FDA-approved drugs, labels, indications, and approval history
Search FDA FAERS for drug safety signals and adverse event reports
Look up gene-drug interactions and dosing guidelines from PharmGKB
Check drug-drug interactions with severity levels and mechanisms
Design, optimize, and predict protein structures using ESM language models and AlphaFold integration.
Generate protein representations with ESM-2 for downstream ML tasks
Predict 3D structures with ESMFold and AlphaFold
Assess mutational effects on stability and function with ESM
Optimize protein sequences for improved activity and stability
Generate entirely new protein sequences for desired functions
Integrate genomics, transcriptomics, proteomics, and metabolomics data for systems biology insights.
Multi-factor analysis combining genomics, proteomics, and metabolomics
Identify diagnostic and prognostic biomarkers with ML
Cross-omics correlation and co-expression network analysis
Build and analyze biological networks to understand drug-target-disease relationships at a systems level.
Build protein-protein interaction networks from STRING database
Visualize KEGG and Reactome pathway crosstalk
Reconstruct transcription factor regulatory networks
Functional enrichment of network modules and communities
A simple, powerful workflow that takes you from hypothesis to results in minutes, not months.
Set up your drug discovery project within your organization. Define indication, stage, and data sensitivity.
Pick from 34+ analysis tools across cheminformatics, genomics, clinical, protein design, and more.
Our MCP server orchestrates 154+ scientific skills — RDKit, DeepChem, ESM, Scanpy, BioPython, and more.
Get structured results, compare runs, generate reports, and share findings with your team.
Whether you are in Big Pharma R&D, a biotech startup, a university lab, or a CRO — BioDevAI adapts to your workflow.
Accelerate your drug pipeline from target identification to IND filing. Run virtual screens, predict ADMET, analyze clinical trial landscapes, and make faster go/no-go decisions.
Access enterprise-grade computational chemistry and bioinformatics without building infrastructure. Focus on your science, not your tech stack.
Empower graduate students and postdocs with publication-ready analysis tools. From genomics to network pharmacology — one platform for all your research.
Deliver faster results to clients with AI-augmented analysis. Standardize workflows across projects, clients, and therapeutic areas.
Search global clinical trial registries, analyze adverse events, check drug interactions, and assess pharmacogenomic implications — all from one dashboard.
Integrate multi-omics data, build interaction networks, discover biomarkers, and uncover pathway crosstalk across your datasets.
Query compounds, proteins, targets, pathways, clinical trials, and genomic variants — all from a single, unified interface.
Bioactivity data for drug-like molecules
Chemical structures and biological activities
Drug and drug target information
Commercially available compounds for virtual screening
Protein sequences, functions, and annotations
Predicted protein structures
Experimentally determined 3D structures
Target-disease association evidence
Protein-protein interaction networks
Metabolic and signaling pathways
Curated biological pathway database
Global clinical trial registry
FDA adverse event reporting system
Genetic variant clinical interpretations
Role-based access control, audit logging, data sensitivity classification (non-PHI, potential-PHI, restricted), and encrypted data storage.
Built on Next.js 15, deployed on Railway with autoscaling. Supabase PostgreSQL for data persistence, Wasabi S3 for file storage.
Organization-based workspaces with granular roles (PI, Scientist, Med Writer, Reg Affairs, QA, Viewer). Invite links and email invitations.
Join researchers at pharmaceutical companies, biotech startups, and universities who are using AI to discover drugs faster. Create your workspace in 30 seconds.