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Workshop
Mon Dec 13 06:00 AM -- 04:00 PM (PST)
Machine Learning in Structural Biology
Ellen Zhong · Raphael Townshend · Stephan Eismann · Namrata Anand · Roshan Rao · John Ingraham · Wouter Boomsma · Sergey Ovchinnikov · Bonnie Berger





Workshop Home Page

Structural biology, the study of proteins and other biomolecules through their 3D structures, is a field on the cusp of transformation. While measuring and interpreting biomolecular structures has traditionally been an expensive and difficult endeavor, recent machine-learning based modeling approaches have shown that it will become routine to predict and reason about structure at proteome scales with unprecedented atomic resolution. This broad liberation of 3D structure within bioscience and biomedicine will likely have transformative impacts on our ability to create effective medicines, to understand and engineer biology, and to design new molecular materials and machinery. Machine learning also shows great promise to continue to revolutionize many core technical problems in structural biology, including protein design, modeling protein dynamics, predicting higher order complexes, and integrating learning with experimental structure determination.

At this inflection point, we hope that the Machine Learning in Structural Biology (MLSB) workshop will help bring community and direction to this rising field. To achieve these goals, this workshop will bring together researchers from a unique and diverse set of domains, including core machine learning, computational biology, experimental structural biology, geometric deep learning, and natural language processing.

Opening remarks
Invited Talk 1: Michael Bronstein (Invited talk)
Invited Talk 2: Cecilia Clementi (Invited talk)
Invited Talk 3: Lucy Colwell (Invited talk)
Structure-aware generation of drug-like molecules (Oral)
Learning physics confers pose-sensitivity in structure-based virtual screening (Oral)
Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs (Oral)
Weakly Supervised Learning for Joint Image Denoising and Protein Localization in Cryo-EM (Oral)
Keynote 1: John Jumper (Keynote speaker)
Poster Session 1 (Poster Session)
Panel Discussion
Keynote 2: Jane Richardson (Keynote speaker)
Break
Predicting cryptic pocket opening from protein structures using graph neural networks (Oral)
End-to-end learning of multiple sequence alignmentswith differentiable Smith-Waterman (Oral)
Function-guided protein design by deep manifold sampling (Oral)
Deciphering antibody affinity maturation with language models and weakly supervised learning (Oral)
Deep generative models create new and diverse protein structures (Oral)
Poster Session 2 (Poster Session)
Invited Talk 4: Derek Lowe (Invited talk)
Invited Talk 5: Regina Barzilay (Invited talk)
Invited Talk 6: Amy Keating (Invited talk)
Closing remarks
Social hour
Exploring ∆∆G prediction with Siamese Networks (Poster)
Dock2D: Toy datasets for the molecular recognition problem (Poster)
AutoFoldFinder: An Automated Adaptive Optimization Toolkit for De Novo Protein Fold Design (Poster)
Adapting protein language models for rapid DTI prediction (Poster)
A kernel for continuously relaxed, discrete Bayesian optimization of protein sequences (Poster)
Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs (Poster)
Active site sequence representation of human kinases outperforms full sequence for affinity prediction (Poster)
Deciphering antibody affinity maturation with language models and weakly supervised learning (Poster)
Structure-aware generation of drug-like molecules (Poster)
Predicting cryptic pocket opening from protein structures using graph neural networks (Poster)
TERMinator: A Neural Framework for Structure-Based Protein Design using Tertiary Repeating Motifs (Poster)
Generative Language Modeling for Antibody Design (Poster)
Predicting single-point mutational effect on protein stability (Poster)
DLA-Ranker: Evaluating protein docking conformations with many locally oriented cubes (Poster)
Studying signal peptides with attention neural networks informs cleavage site predictions (Poster)
Weakly Supervised Learning for Joint Image Denoising and Protein Localization in Cryo-EM (Poster)
End-to-end learning of multiple sequence alignmentswith differentiable Smith-Waterman (Poster)
MOLUCINATE: A Generative Model for Molecules in 3D Space (Poster)
Protein sequence sampling and prediction from structural data (Poster)
Interpretable Pairwise Distillations for Generative Protein Sequence Models (Poster)
HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints (Poster)
Real-valued Sidechain Dihedrals Prediction Using Relation-Shape Convolution (Poster)
Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction (Poster)
Deep generative models create new and diverse protein structures (Poster)
Residue characterization on AlphaFold2 protein structures using graph neural networks (Poster)
Function-guided protein design by deep manifold sampling (Poster)
Turning high-throughput structural biology into predictive drug design (Poster)
MSA-Conditioned Generative Protein Language Models for Fitness Landscape Modelling and Design (Poster)
Learning physics confers pose-sensitivity in structure-based virtual screening (Poster)