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drug-discovery

topic8 events
papersTODAY 04:00 UTC

Ensemble-Conditioned Molecular Design Accounts for Conformer Distributions

A new arXiv preprint argues that molecular design should not be reduced to finding candidates that lock into one bioactive shape, since real molecules exist across a range of conformations. The authors propose an ensemble-conditioned method that designs molecules against this distribution of shapes rather than a single structure, aiming to better capture the properties that determine whether a candidate succeeds.

molecular-design/arxiv/generative-models/chemistry
papersTODAY 04:00 UTC

Chemical and geometric representation fidelity tied to better drug-target affinity models

A new arXiv paper argues that drug-target binding affinity prediction improves when models preserve both chemical and geometric details of the interacting molecules. The authors focus on representation fidelity as a way to capture the subtle structural features that determine molecular recognition. The work falls under machine learning research rather than a released product or model.

drug-discovery/representation-learning/molecular-modeling/research
papersSEP 12 04:00 UTC

Multi-Layer Knowledge Graph Proposed for CMC Process Development

A new arXiv paper describes a multi-layer knowledge graph intended to consolidate the fragmented technical records produced during chemistry, manufacturing and controls (CMC) process development. The approach aims to move organizations away from isolated document repositories toward structured process intelligence spanning drug discovery through commercial manufacturing. The work targets the knowledge-intensive, multi-stage nature of pharmaceutical development.

knowledge-graph/pharma/manufacturing/arxiv
papersSEP 11 04:00 UTC

LLM-as-a-Judge Framework for Agentic AI in Drug Discovery Aligned With Human Raters

A new arXiv paper addresses the difficulty of scoring open-ended, tool-using LLM agents in chemistry and drug discovery, where conventional benchmarks fall short. The authors propose an evaluation system built on the LLM-as-a-Judge approach and tune it against human expert judgments to improve reliability. The work aims to make automated assessment of agentic scientific workflows more trustworthy.

llm-evaluation/agents/drug-discovery/benchmarks
papersSEP 11 04:00 UTC

Fixed-Dimensional Latent Flow Model Generates Variable-Size 3D Molecules

Researchers present a transformer-based autoencoder that maps 3D molecules of differing sizes into a single fixed-dimensional latent space, removing the need to fix molecule size in advance. This matters because molecular size is tied to composition, structure and other target properties in drug and materials discovery. The approach is described as equivariant-free, meaning it avoids the geometric constraints commonly used in 3D molecular generators.

molecules/generative-models/3d-generation/research
papersSEP 10 04:00 UTC

Study systematically benchmarks molecule generation models for de novo drug design

A new arXiv study presents a systematic evaluation of molecule generation models, which are computational tools for exploring chemical space in de novo drug design beyond what traditional virtual screening allows. The authors compare leading approaches across benchmarks and distill practical insights to guide real-world use in drug discovery.

drug-discovery/benchmarks/molecule-generation
papersSEP 10 04:00 UTC

Explainable ML Framework Predicts Blood-Brain Barrier Permeability from Molecular Descriptors

A new arXiv paper presents an explainable machine learning framework that predicts blood-brain barrier permeability using molecular descriptors. Since this barrier determines whether central nervous system drug candidates can reach targets in the brain, the approach could support earlier screening in drug development. Its explainable design is intended to reveal which molecular features drive the model's predictions.

explainability/machine-learning/drug-discovery/healthcare
papersSEP 10 04:00 UTC

Small Molecule Optimization with Large Language Models

A research paper on arXiv examines how large language models can be applied to molecular optimization, the task of designing small molecules with targeted properties. The work positions LLM-based approaches as a promising tool for drug discovery pipelines, where candidate compounds must be iteratively refined to meet specific criteria.

drug-discovery/llm/chemistry/research