Alec Glisman, Ph.D.
I am a Senior AI/ML Scientist at Merck & Co., applying generative AI and deep learning to design and optimize small-molecule therapeutics. I bridge cheminformatics, physics-based simulation, and machine learning to accelerate drug discovery from hit identification through lead optimization.
Ph.D. in Chemical Engineering from Caltech (2024), where I studied charged-polymer physics through molecular simulation.
Research Highlights
Generative AI & Molecular Design
AI/MLDrug Discovery
GFlowNet-based generative models for R-group optimization and core-hopping, constrained to validated reaction templates and commercially available building blocks.
Molecular Property Prediction
AI/MLCheminformatics
Automated benchmarking pipeline across multiple architectures for ADMET prediction. 25th / 100 finalists in OpenADMET Blind Challenge.
Polyelectrolyte Simulations & Ion Binding
PhysicsSimulation
Enhanced-sampling MD revealing ion-ion correlations as the primary driver of polyelectrolyte attraction, with autoencoder analysis of complex structures.
Focus Areas
Generative AI Molecular Design De Novo Design Lead Optimization ADMET Prediction Graph Neural Networks Deep Learning Multi-Objective Optimization Drug Discovery Cheminformatics Property Prediction Computational Chemistry Structure-Activity Relationships Molecular Dynamics
Education
| Ph.D., Chemical Engineering | California Institute of Technology, 2024 |
| MS, Chemical Engineering | California Institute of Technology, 2022 |
| BS, Chemical Engineering | University of California, Berkeley, 2019 |