90% of drug candidates fail during development. It’s time we change the maths!
If you’re in lead optimization or planning IND-enabling work, I can offer a free 48-hour ADMET benchmark on 5 compounds (SMILES) to show signal on your own chemistry—no obligation. We can provide end to end service for drug discovery and clinical stages of drug development.
As the CSO at Prognica Labs, I engage with discovery teams weekly who are facing common industry challenges: unpredictable ADMET liabilities, late-stage toxicity failures, and lengthy synthesis cycles.
We developed Prognica’s AI/ML platform to address these issues. By integrating predictive models directly into workflows, we assist biotechs and pharma companies in reducing synthesis cycles by 40-60% and accelerating hit-to-lead timelines by over 6 months.
Currently, our partners are experiencing the fastest ROI in three key areas:
🔹 ADMET Hit-to-Lead Optimization: Simultaneous optimization of potency and ADMET properties, generating 500-1,000 viable analogs per scaffold.
🔹 DDI Risk Prediction: Over 80% accuracy in predicting CYP450/transporter interactions and clinical DDI magnitude before IND filing.
🔹 Clinical PK Prediction: Highly accurate human PK predictions (clearance, Vd, t1/2) derived directly from preclinical data.
Our capabilities extend beyond standard small molecules, supporting end-to-end R&D from early discovery through clinical-stage development:
🔬 Target ID & Hit Discovery:
• NLP-driven target validation & literature mining
• Phenotypic screening hit ID & target deconvolution
• AI-guided Fragment-Based Drug Design (FBDD)
💊 Novel Modalities & Complex Targets:
• De Novo design of PROTACs and molecular glues
• Generative AI for Macrocycles (Beyond RO5) & Peptide therapeutics
• RNA-targeted small molecule design
• Antibody humanization & affinity maturation
⚙️ Development, Formulation & Clinical Strategy:
• Solid form selection & formulation development AI
• Oral drug absorption prediction (PBPK-ML hybrid)
• AI-powered reaction prediction & retrosynthetic planning
• Drug repurposing & synergistic combination discovery for clinical pipelines
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