Search Results for author:
Found 5 papers, 3 papers with code
Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design
Specifically, we first pre-train an antibody language model based on the sequence data, then propose a one-shot way for sequence and structure generation of CDR to avoid the heavy cost and error propagation from an autoregressive manner, and finally leverage the pre-trained antibody model for the antigen-specific antibody generation model with some carefully designed modules.
Tailoring Molecules for Protein Pockets: a Transformer-based Generative Solution for Structured-based Drug Design
Structure-based drug design is drawing growing attentions in computer-aided drug discovery.
SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction
Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities.
Ranked #1 on
Drug Discovery
on KIBA
Direct Molecular Conformation Generation
Molecular conformation generation aims to generate three-dimensional coordinates of all the atoms in a molecule and is an important task in bioinformatics and pharmacology.
