Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
A systematic survey in geometric deep learning for structure-based drug design
Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to
identify potential drug candidates. Traditional methods, grounded in physicochemical …
identify potential drug candidates. Traditional methods, grounded in physicochemical …
Convert: Contrastive graph clustering with reliable augmentation
Contrastive graph node clustering via learnable data augmentation is a hot research spot in
the field of unsupervised graph learning. The existing methods learn the sampling …
the field of unsupervised graph learning. The existing methods learn the sampling …
Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening
Conspectus Molecular docking, also termed ligand docking (LD), is a pivotal element of
structure-based virtual screening (SBVS) used to predict the binding conformations and …
structure-based virtual screening (SBVS) used to predict the binding conformations and …
3D molecular generative framework for interaction-guided drug design
Deep generative modeling has a strong potential to accelerate drug design. However,
existing generative models often face challenges in generalization due to limited data …
existing generative models often face challenges in generalization due to limited data …
Lo-hi: Practical ml drug discovery benchmark
S Steshin - Advances in Neural Information Processing …, 2023 - proceedings.neurips.cc
Finding new drugs is getting harder and harder. One of the hopes of drug discovery is to use
machine learning models to predict molecular properties. That is why models for molecular …
machine learning models to predict molecular properties. That is why models for molecular …
Knowledge distillation for high dimensional search index
Lightweight compressed models are prevalent in Approximate Nearest Neighbor Search
(ANNS) and Maximum Inner Product Search (MIPS) owing to their superiority of retrieval …
(ANNS) and Maximum Inner Product Search (MIPS) owing to their superiority of retrieval …
Mixed graph contrastive network for semi-supervised node classification
Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised
node classification in recent years. However, the problem of insufficient supervision …
node classification in recent years. However, the problem of insufficient supervision …
An equivariant generative framework for molecular graph-structure co-design
Designing molecules with desirable physiochemical properties and functionalities is a long-
standing challenge in chemistry, material science, and drug discovery. Recently, machine …
standing challenge in chemistry, material science, and drug discovery. Recently, machine …
Binding-Adaptive Diffusion Models for Structure-Based Drug Design
Abstract Structure-based drug design (SBDD) aims to generate 3D ligand molecules that
bind to specific protein targets. Existing 3D deep generative models including diffusion …
bind to specific protein targets. Existing 3D deep generative models including diffusion …