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Found 9 papers, 3 papers with code
AVIDa-hIL6: A Large-Scale VHH Dataset Produced from an Immunized Alpaca for Predicting Antigen-Antibody Interactions
However, the publicly available datasets in existing works have notable limitations, such as small sizes and the lack of non-binding samples and exact amino acid sequences.
Meaningful machine learning models and machine-learned pharmacophores from fragment screening campaigns
In this study we derive ML models from over 50 fragment-screening campaigns to introduce two important elements that we believe are absent in most -- if not all -- ML studies of this type reported to date: First, alongside the observed hits we use to train our models, we incorporate true misses and show that these experimentally validated negative data are of significant importance to the quality of the derived models.
Rethinking Attention with Performers
We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity, without relying on any priors such as sparsity or low-rankness.
Ranked #7 on
Offline RL
on D4RL
Attribution Methods Reveal Flaws in Fingerprint-Based Virtual Screening
Our results confirm that high-performing models may not learn the correct binding rule, and suggest concrete steps that can remedy this situation.
Population-Based Black-Box Optimization for Biological Sequence Design
The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences--a setting that off-the-shelf black-box optimization methods are ill-equipped to handle.
Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers
In response, solutions that exploit the structure and sparsity of the learned attention matrix have blossomed.
Model-based reinforcement learning for biological sequence design
In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design.
Model-based Reinforcement Learning
reinforcement-learning
+1
Noisy, sparse, nonlinear: Navigating the Bermuda Triangle of physical inference with deep filtering
Capturing the microscopic interactions that determine molecular reactivity poses a challenge across the physical sciences.
Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry
The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding.
