👋 Hi there, I'm Alec Glisman

I bridge physics-based simulations with machine learning architectures to accelerate molecular design and discovery.

I’m open to collaboration across AI for Science, molecular modeling, and generative chemistry. Let’s connect and build the next generation of molecular design together.

🔗 LinkedIn · Google Scholar · arXiv · ORCID

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🔬 About Me

💊 Senior Machine Learning Scientist @ Merck & Co.
Applying predictive deep learning models and generative AI to accelerate drug discovery by designing, predicting, and optimizing molecular therapeutics for real-world impact.

🎓 Ph.D. in Chemical Engineering @ Caltech (Wang & Brady Groups)
Developed molecular simulations of polyelectrolytes and multivalent ions to uncover mechanisms of mineralization, adsorption, and polymer–surface interactions using state-of-the-art enhanced sampling techniques.

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📚 Publications

• 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

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🧠 Machine Learning

🧩 Representations & Embeddings

• Molecular formats: SMILES, molecular graphs, 3D conformers, fingerprints
• Augmentation: SMILES permutation, conformer sampling, noise injection
• Pre-trained embeddings: ChemBERTa, CheMeleon

🏗️ Architectures

• Graph Neural Networks: GCNs, MPNNs, GATs
• Generative Models: VAEs, Normalizing Flows, GFlowNets, Diffusion Models, Transformers
• Convolutional Networks: ResNet, DenseNet, U-Net, ViT
• Recurrent Networks: GRU, BiLSTM
• Classical ML: Random Forests, Gradient Boosting, SVMs, kNN, Gaussian Processes

🧭 Training Strategies

• Reinforcement Learning: Policy gradient, reward shaping, exploration and exploitation
• Transfer Learning: Pre-training, domain adaptation
• Unsupervised Learning: Clustering (k-means, hierarchical), dimensionality reduction (PCA, t-SNE, UMAP)
• Uncertainty Quantification: Ensembles, mean–variance estimation, evidential deep learning, MC dropout

⚙️ Frameworks & Tools

🧠 Machine Learning

🧬 Chemistry & Simulations

🧰 Data Science & DevOps

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⚛️ Physics-Based Simulations

🧬 Molecular Dynamics

• GROMACS for all-atom and coarse-grained MD simulations
• PLUMED for enhanced sampling (metadynamics, umbrella sampling, OPES)

⚗️ Quantum Chemistry

• ORCA for energy calculations and geometry optimizations

🧪 Docking

• Schrödinger Suite, GLIDE, and AutoDock Vina for ligand–protein docking and screening

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💻 Code Development & Infrastructure

🧠 Languages

Python · C++ · CUDA · SQL · Shell · LaTeX · FORTRAN

🧩 Environment & Deployment

• HPC Scheduling: Slurm, PBS
• Provisioning: Ansible, Spack
• Containerization: Docker with persistent volumes
• Version Control: Git, GitHub, GitHub Actions (CI/CD)
• Editors: VS Code, Vim, Jupyter