Motivation: The scalable identification of bioactive compounds is essential for contemporary drug discovery. This process faces a key trade-off: structural screening offers scalability but lacks biolo...
Recent advances in drug discovery have demonstrated that incorporating side information (e.g., chemical properties about drugs and genomic information about diseases) often greatly improves prediction...
Predicting the effects of chemical and genetic perturbations on quantitative cell states is a central challenge in computational biology, molecular medicine and drug discovery. Recent work has leverag...
Hit-to-lead optimization requires iterative design of hit analogs across competing potency, selectivity, physicochemical, pharmacokinetic, safety, and synthetic constraints. We present SABLE (Syntheti...
Scientific discovery increasingly relies on AI systems to select candidates for expensive experimental validation, yet no principled, budget-aware evaluation framework exists for comparing selection s...
The rapid growth of molecular foundation models and general-purpose large language models has encouraged a scale-centric view of artificial intelligence in drug discovery, in which larger pretrained m...
Recent advances in machine learning and large-scale biological data collections have revived the prospect of building a virtual cell, a computational model of cellular behavior that could accelerate b...
Large Language Models (LLMs) have the potential to accelerate small molecule drug design due to their ability to reason about information from diverse sources and formats. However, their practical uti...
Antibody lead optimization is inherently a multi-objective challenge in drug discovery. Achieving a balance between different drug-like properties is crucial for the development of viable candidates, ...
Accurate prediction of binding sites of a given protein, to which ligands can bind, is a critical step in structure-based computational drug discovery. Recently, Equivariant Graph Neural Networks (GNN...
Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-based generative approac...
Seahorses (Hippocampus spp.) have been used in traditional Chinese medicine for centuries and are now regarded as underexplored but promising marine resources for modern drug discovery. This review re...
The central problem in biomedical imaging are batch effects: systematic technical variations unrelated to the biological signal of interest. These batch effects critically undermine experimental repro...
The escalating threat of antimicrobial resistance (AMR) has created an urgent need for new antimicrobial agents. Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics du...
This review systematically analyzes 186 valid data points derived from 157 distinct marine natural small-molecule drug leads discovered in China in the past decade (2015-2024). These compounds have al...
Geodia cydonium, a sponge belonging to Demospongiae and endemic species to the Mediterranean Sea, is capturing increasing attention due to its rich and chemically diverse metabolome. Previous studies ...
Natural products from marine and terrestrial organisms provide an important resource for medicinal chemistry and drug discovery. Defined as occurring at depths greater than 200 m, most of the ocean, b...
In recent years, Marine Natural Products (MNPs) have emerged as a significant source for anticancer drug discovery, as many natural products can offer structural diversity, unique mechanisms of action...
Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease cont...
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