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...
Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining a similar overall st...
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...
Marine organisms from deep-sea environments have attracted considerable attention in drug discovery because they produce structurally diverse natural products. However, Norwegian deep-sea ecosystems r...
Marine fungal natural products are valuable sources of structurally diverse and biologically active compounds with potential applications in drug discovery and biotechnology. The fungus Penicillium cr...
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...
Deep-sea-derived fungi have emerged as a prolific source of structurally novel and pharmacologically active secondary metabolites, representing an invaluable reservoir of lead compounds for drug disco...
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...
Drug discovery for neurological disease has traditionally centered on the molecules altered by disease. But the molecules that cause pathology are not necessarily the best points from which to reverse...
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