From target discovery to IND filing, 8 AI agents cover the entire drug development lifecycle. Each phase has specialized agent collaboration, reducing traditional 4-6 year cycles to 18-24 months.
AI-driven target mining from literature and multi-omics data to identify high-value targets
NLP engine scans PubMed, ClinicalTrials.gov and 20+ databases daily, automatically extracting gene-disease-drug triple relationships to build real-time knowledge graphs.
Integrates genomics, transcriptomics, proteomics, and metabolomics data. Graph neural networks identify core disease pathways and potential intervention targets.
Based on AlphaFold2 structure prediction and pocket detection algorithms, evaluates target protein druggability and predicts feasibility across modalities (small molecule, antibody, PROTAC).
Rapid screening of hit compounds from million-scale compound libraries
Dual strategy combining structure-based (SBVS) and ligand-based (LBVS) screening. DrugCLIP deep learning model achieves rapid primary screening of 1M+ compounds daily.
Transformer and diffusion model-based molecular generation engine designs novel structures satisfying multi-objective constraints (activity, druggability, synthetic feasibility).
Proprietary Glide-Style docking engine with deep learning scoring function. Accurate prediction of protein-ligand binding modes and free energy. Supports flexible and covalent docking.
AI-guided multi-round iterative optimization balancing activity, selectivity, and drug-likeness
Five-dimensional prediction covering absorption, distribution, metabolism, excretion, and toxicity. Ensemble model trained on 200+ ADMET datasets. Covers hERG, CYP450, Ames and key endpoints.
Automated structure-activity relationship analysis identifying key pharmacophores and toxicophores. AI recommends next modification directions, reducing trial-and-error costs.
AI retrosynthetic analysis automatically plans optimal synthesis routes. Evaluates synthetic feasibility (SA Score) and cost, recommending commercial reagent suppliers.
Bridging from in vitro to in vivo, from animal to human studies
AI predicts acute, chronic, reproductive, and genotoxicity across multiple dimensions. Integrates Tox21/ToxCast data to reduce animal study risks.
Automatically generates CMC, pharmacology-toxicology, and clinical protocol documentation for IND filing. ReguBot Agent ensures FDA/NMPA regulatory compliance.
AI-assisted formulation optimization, process parameter design, and stability prediction. Digital twin simulates scale-up manufacturing, reducing process development time.
From millions of compounds to a single clinical candidate
Interactive charts showing pipeline metrics and timelines
DrugMind has helped multiple pharma companies reduce R&D cycles by 40% and costs by 60%
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