drug-discovery

AWS and Novo Nordisk expand their partnership, with Novo Nordisk selecting AWS as its preferred cloud provider and strategic AI partner to accelerate drug discovery using AI tools like Amazon Bio Discovery and Bedrock AgentCore, plus a co-innovation hub in London.

This paper presents CAi Copilot, an expert-oriented agent with three linked layers that turns molecular design intent into executable, traceable workflows, achieving the strongest performance across 45 tasks.

MolBioKG is a two-layer system that grounds unseen molecules in biomedical knowledge graphs via multi-resolution structural anchoring, enabling out-of-graph link recovery and multi-hop reasoning from SMILES strings. It significantly improves Hits@10 and out-of-graph target recall over baselines while maintaining traceable evidence.

A Nature Reviews Drug Discovery perspective critically reviews AI in drug discovery, noting limited clinical impact and offering recommendations for improving translational relevance.

EpiBench is a new closed-book, sequence-based benchmark for evaluating how well LLMs understand epitopes across five antibody-drug-discovery tasks, finding that current models capture partial signals but struggle with antibody-specific reasoning.

This paper introduces BBBP-GeoPEFT, a geometry-informed parameter-efficient fine-tuning framework for pre-trained molecular GNNs targeting blood-brain barrier permeability prediction, achieving competitive performance while updating only 10.1% of parameters.

Olio Labs is introducing its in vivo platform, designed to predict diverse human clinical outcomes from a single high-throughput experiment, bringing predictive validity to drug discovery.

This paper proposes PhenMol, a structure-preserving framework for phenotype-aware molecular representation learning that integrates cellular phenotype information while preserving chemical structure neighborhood organization, improving molecular property prediction and drug discovery tasks.

A new Science study reports streamlined access to an underexplored covalent warhead type with well-tempered reactivity, enabling practical use in drug discovery.

Discussion on which area of healthcare will see the biggest transformation from AI in the next few years, including diagnosis, drug discovery, patient monitoring, and medical imaging.

AI accelerates drug discovery by predicting candidates and reducing costs, but success depends on high-quality data and integration with lab systems to close the data loop and validate predictions.

Tsinghua University and AIRTHU introduce GalaxyVS, a method that transforms protein–ligand docking into ultra-large-scale parallel vector retrieval, setting a new global benchmark for high-throughput drug discovery.

TriGlue is a biology-inspired generative model that designs molecular glue-induced ternary complexes by first predicting protein-protein interfaces and then generating molecular glues conditioned on the interface, enabling targeted protein degradation.

This review surveys geometric deep learning (GDL) approaches for polypharmacology and multi-target drug design, covering architectures from graph neural networks to SE(3)-equivariant diffusion models for capturing 3D molecular structures.

This paper proposes ChemHyperMag, a physics-informed magnetic hypergraph learning method for multitask ADMET prediction that uses functional group hypergraphs and a Hermitian magnetic Laplacian to capture asymmetric interactions and directional signals, improving prediction accuracy with fewer labeled samples.

This paper proposes a preference-based learning framework for antibody expression ranking, integrating scarce quantitative data with large-scale weak positive supervision from immunization sequences. The method adapts Direct Preference Optimization to protein language models using a union-masked log-likelihood approximation and IMGT-based alignment, achieving improved ranking performance on a diverse internal dataset.

Bristol Myers Squibb is deploying its second NVIDIA DGX SuperPOD based on DGX Vera Rubin NVL72 systems, creating the most powerful AI cluster in life sciences to accelerate drug discovery and make AI accessible to all scientists.

This paper proposes a Context-Augmented Prompting framework that uses a GNN expert model to provide predictive hints and explanatory subgraphs to improve molecular property prediction in small language models. Experiments on MUTAG and Tox21 show accuracy gains of up to 74% over SMILES-only baselines.

Introduces HEDGEHOG, a hierarchical benchmark for evaluating molecular generative models in drug discovery, revealing that only 0.65% of generated molecules pass all medicinal chemistry and docking filters.

Michael Antonov, co-founder of Oculus, discusses his pivot to drug discovery through Deep Origin, emphasizing the integration of AI with physics-based simulations and experimental validation for safer therapies, and the role of VR in medical training.