I bridge physics-based simulations and cheminformatics with machine learning to accelerate molecular design and drug discovery. My background spans fluid mechanics, polymer physics, and generative AI.
I'm open to collaborating on AI for Science, molecular modeling, and generative chemistry. Reach out if you're working in these areas.
🔗 Website · LinkedIn · Google Scholar · arXiv · ORCID
- 🥉 Top 3% (10th / 356) in the OpenADMET-ExpansionRx blind challenge for ADMET property prediction
- 🧬 Built and deployed generative models for molecular designs for R-group optimization and core-hopping over validated reaction templates.
- 📄 5 peer-reviewed papers spanning polymer physics, fluid mechanics, and biophysics
- 🎓 Ph.D. in Chemical Engineering from Caltech, advised by Zhen-Gan Wang & John F. Brady
💊 Senior AI/ML Scientist @ Merck & Co. Modeling & Informatics Group
Applying predictive deep learning models and generative AI to accelerate drug discovery by designing, predicting, and optimizing small-molecule therapeutics.
🎓 Ph.D. Chemical Engineering, Caltech (2024)
🎓 M.S. Chemical Engineering, Caltech (2022)
🎓 B.S. Chemical Engineering, UC Berkeley (2019)
| Repository | Description |
|---|---|
| OpenADMET-ExpansionRx | ADMET stacked ensemble models — top 3% (10th / 356) in the OpenADMET-ExpansionRx blind challenge |
| DDPM-Enhanced-Sampling | Denoising diffusion models for Boltzmann-consistent polymer conformational sampling |
| Swimming-in-Potential-Flow | C++/CUDA boundary integral methods — companion code for JFM 2022 paper |
| Analysis-Polyelectrolyte-Surface-Adsorption | Analysis pipeline for interfacial polyelectrolyte MD simulations |
- Binding Modes and Water-Mediation of Polyelectrolyte Adsorption to a Neutral Calcium Carbonate Surface — Langmuir, 2025 DOI
- Multivalent Ion-Mediated Polyelectrolyte Association and Structure — Macromolecules, 2024
DOI, arXiv - Adsorption Isotherm and Mechanism of Calcium-Ion Binding to Polyelectrolyte — Langmuir, 2024
DOI, arXiv - Swimming in Potential Flow — J. Fluid Mech., 2022
DOI - Geometry and Dynamics of Lipid Membranes: The Scriven–Love Number — Phys. Rev. E, 2020
DOI, arXiv
Core Areas: Graph neural networks (GCNs, MPNNs, GATs) · Generative models (VAEs, diffusion, GFlowNets, transformers + RL) · Uncertainty quantification · Transfer & curriculum learning · Conformer ensemble models
- Drug Discovery: Structure-based & ligand-based design, de novo generation, lead optimization, multi-parameter optimization (MPO), ADMET prediction, synthesizability screening, virtual screening
- Representations: SMILES, InChI, molecular graphs, 3D conformers, ECFP fingerprints, pharmacophores, pre-trained embeddings (ChemBERTa, CheMeleon)
- Databases & Tools: ChEMBL, PubChem, DrugBank; Schrödinger Suite, AutoDock Vina
- Molecular Dynamics: Enhanced sampling (metadynamics, OPES, umbrella sampling, HREX), MDAnalysis, Markov state modeling, free-energy calculations
- Continuum & Quantum: Potential flow theory, microswimmer hydrodynamics, lipid membrane mechanics (Scriven–Love); ORCA for QM; Schrödinger/GLIDE for docking
Python · C++ · CUDA · SQL · Shell · LaTeX · FORTRAN
Slurm · PBS · Ansible · Spack · Git
📄 For more on my research, experience, and publications, visit alec-glisman.github.io