174 citations · 574 across the 10 of their papers we have counts for
10 papers
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions
Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda +8
Antibodies have become an important class of therapeutic agents to treat human diseases. To accelerate therapeutic antibody discovery, computational methods, especially machine lea…
Meaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns
Carl Poelking, Gianni Chessari, Christopher W. Murray +3
Machine learning (ML) is widely used in drug discovery to train models that predict protein-ligand binding. These models are of great value to medicinal chemists, in particular if…
Rethinking Attention with Performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan +10
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed t…
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan +8
Transformer models have achieved state-of-the-art results across a diverse range of domains. However, concern over the cost of training the attention mechanism to learn complex dep…
Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering
Carl Poelking, Yehia Amar, Alexei Lapkin +1
Capturing the microscopic interactions that determine molecular reactivity poses a challenge across the physical sciences. Even a basic understanding of the underlying reaction mec…
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry
Kevin McCloskey, Ankur Taly, Federico Monti +2
Deep neural networks have achieved state of the art accuracy at classifying molecules with respect to whether they bind to specific protein targets. A key breakthrough would occur…
Optimal design of experiments by combining coarse and fine measurements
Alpha A. Lee, Michael P. Brenner, Lucy J. Colwell
In many contexts it is extremely costly to perform enough high quality experimental measurements to accurately parameterize a predictive quantitative model. However, it is often mu…
Inferring interaction partners from protein sequences
Anne-Florence Bitbol, Robert S. Dwyer, Lucy J. Colwell +1
Specific protein-protein interactions are crucial in the cell, both to ensure the formation and stability of multi-protein complexes, and to enable signal transduction in various p…
Protein sectors: statistical coupling analysis versus conservation
Tiberiu Tesileanu, Lucy J. Colwell, Stanislas Leibler
Statistical coupling analysis (SCA) is a method for analyzing multiple sequence alignments that was used to identify groups of coevolving residues termed "sectors". The method appl…
3D Protein Structure Predicted from Sequence
Debora S. Marks, Lucy J. Colwell, Robert Sheridan +4
The evolutionary trajectory of a protein through sequence space is constrained by function and three-dimensional (3D) structure. Residues in spatial proximity tend to co-evolve, ye…