| 8/26 | Introduction | | |
| Sequence Modelling and Generation with Transformer slides | | |
| 9/2 | Modeling Structure with Equivariant Graph Neural Networks and Diffusion Models | | |
| Single-cell biology Research Overview | | |
| 9/9 | Small Molecule Design: MARS: Markov Molecular Sampling for Multi-objective Drug Discovery; Multi-Objective Molecule Generation using Interpretable Substructures | | |
| A 3D Generative Model for Structure-Based Drug Design; Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets; Structure-based drug design with equivariant diffusion models; Reinforced genetic algorithm for structure-based drug design | | |
| 9/16 | ESM and ProGen | | |
| Importance Weighted Expectation-Maximization for Protein Sequence Design; Proximal Exploration for Model-guided Protein Sequence Design | | |
| 9/23 | Robust deep learning based protein sequence design using ProteinMPNN; A Deep SE(3)-Equivariant Model for Learning Inverse Protein Folding | | |
| Scaffolding protein functional sites using deep learning; Generative Enzyme Design Guided by Functionally Important Sites and Small-Molecule Substrates | | |
| 9/30 | Protein Structure Prediction: AlphaFold/AlphaFold2 | | |
| De novo design of protein structure and function with RFdiffusion; Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem; PPDiff: Diffusing in Hybrid Sequence-Structure Space for Protein-Protein Complex Design | | |
| 10/7 | Enzyme Design: De novo design of luciferases using deep learning | | |
| Accurate structure prediction of biomolecular interactions with AlphaFold 3, RosettaAllAtom | | |
| 10/14 | Fall break, no class | | |
| 10/21 | DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome; Nucleotide Transformer; Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution; Dnabert-2: Efficient foundation model and benchmark for multi-species genome | | |
| Predicting DNA structure using a deep learning method | | |
| 10/28 | CodonBERT: Large Language Models for mRNA design and optimization | | |
| A sequence-based global map of regulatory activity for deciphering human genetics | | |
| 11/4 | Base-resolution models of transcription-factor binding reveal soft motif syntax | | |
| Design of highly functional genome editors by modeling the universe of CRISPR-Cas sequences | | |
| 11/11 | A programmable reaction-diffusion system for spatiotemporal cell signaling circuit design | | |
| Transfer learning enables predictions in network biology | | |
| 11/18 | scGPT: toward building a foundation model for single-cell multi-omics using generative AI; Universal Cell Embeddings: A Foundation Model for Cell Biology | | |
| Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back | | |
| 11/25 | A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models | | |
| End-to-End Full-Atom Antibody Design; Conditional Antibody Design as 3D Equivariant Graph Translation; Atomically accurate de novo design of single-domain antibodies | | |
| 12/2 | Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii | | |
| Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells | | |