arXiv Machine Learning

D2ML: Models for Direct-to-Biology Small Molecule Screening

The paper introduces D2ML, a set of machine‑learning models designed for Direct‑to‑Biology (D2B) small‑molecule screening. These assay‑aware models predict activity from single‑point screens, relative potency across targets from crude dose‑response assays, and absolute potency from purified‑compound measurements, and a joint model that shares information across stages improves predictions, especially for crude dose‑response selectivity. Using these models, the authors guided the synthesis of 84 new compounds that extended the Pareto front of potency and selectivity, achieving a ROS1 IC₅₀ of 14.7 nM with 233‑fold selectivity over TRKB, surpassing the best training compound.

arXiv Machine Learning

5d ago

Predicting Dose-Response Success from Primary Screening Data

The study explores whether inexpensive three‑point primary‑screening data can predict the success of more costly 11‑point dose‑response experiments in determining compound potency (pXC50). Using a dataset of 32,971 compound‑assay pairs, random‑forest models achieved an AUROC of 0.939 and successfully identified 90% of reportable potencies while reducing follow‑up profiling to 55.7% of historically promoted pairs. The results indicate that primary‑screen response features, especially response amplitude, are more predictive of dose‑response quantifiability than molecular structure features.

By Sean Lim

efficiency

arXiv AI

Oct 19

DrugHarness: An Evolvable Agent Harness for Computational Drug Discovery

arXiv:2610.23791v1 Announce Type: new Abstract: Computational drug discovery requires agents to compose scientific operations and adapt to changing evidence and constraints. Here we introduce DrugHar...

By Yuxing Chen, Zhisheng Zhang, Cheng Tang, Wei Li, Minghui Zhang, Ziyan Huang, Shaocong Wei, Jiawei Chen, Junru Hu, Hongze Zhu, Jianghan Shen, Min Cen, Xinyuan Zhu, Yaxue Zhao, Jubo Wang, Yuqiang Li, Chun-Mei Feng, Zongyuan Ge, Rui Su, Jie Xu, Yirong Chen, Xi Xu, Ming Hu

agents

arXiv Machine Learning

Oct 19

AceFF: A State-of-the-Art Machine Learning Potential for Small Molecules

arXiv:2601.00581v3 Announce Type: replace-cross Abstract: We introduce AceFF, a pre-trained machine learning interatomic potential (MLIP) optimized for small molecule drug discovery. While MLIPs have...

By Stephen E. Farr, Stefan Doerr, Antonio Mirarchi, Alireza Moradzadeh, Francesc Sabanes Zariquiey, Saee Paliwal, Gianni De Fabritiis

efficiency benchmarks

arXiv Machine Learning

Jul 7

APEX: Approximate-but-exhaustive search for ultra-large combinatorial synthesis libraries

arXiv:2510. 24380v2 Announce Type: replace Abstract: Make-on-demand combinatorial synthesis libraries (CSLs) like Enamine REAL have significantly enabled drug discovery efforts.

By Aryan Pedawi, Jordi Silvestre-Ryan, Bradley Worley, Darren J Hsu, Kushal S Shah, Elias Stehle, Jingrong Zhang, Izhar Wallach

benchmarks

arXiv Machine Learning

Aug 28

Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling

The paper introduces a framework to predict whether a compound’s potency can be quantified in dose‑response profiling, treating quantifiability as a separate triage goal from biological activity. It shows that features from low‑cost primary screens, rather than molecular structure, strongly predict quantifiability, and that this prediction holds across new chemical scaffolds and assay families. The authors argue that incorporating quantifiability predictions can better allocate expensive dose‑response resources.

By Sean Lim

efficiency

arXiv Machine Learning

Aug 13

Large-scale AI-Ready Data for Anti-Cancer Drug Response Modeling

arXiv:2608. 11444v1 Announce Type: cross Abstract: Drug response prediction (DRP) models are an active area of research in pharmacogenomics, with growing potential to accelerate the identification of effective anticancer drugs.

By Vincent Lavelle, Yitan Zhu, Kaitlyn Marlor, Thomas Brettin, Rick Stevens

benchmarks

arXiv AI

Jul 10

DrugGen 2: A disease-aware language model for enhancing drug discovery

arXiv:2607. 08404v1 Announce Type: cross Abstract: 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 context on target behavior and therapeutic outcomes.

By Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami, Navid Mazrouei, Matin Irajpour, Yousof Gheisari, Hajar Sirous

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Sep 22

Adapting Boltz-2 with limited experimental activity data improves early enrichment in virtual screening

arXiv:2609.24302v1 Announce Type: cross Abstract: Virtual screening aims to prioritize active compounds from large chemical libraries within a limited experimental budget. When applying Boltz-2 to vi...

By Kairi Furui, Masahito Ohue

rag fine-tuning

Hugging Face Trending Papers

Jul 14

Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization

Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design. Many such models follow the structure-based drug design (SBDD) paradigm, generating molecules to fit a target binding pocket.

diffusion benchmarks

arXiv Machine Learning

Jul 15

Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization

arXiv:2607. 12349v1 Announce Type: new Abstract: Drug discovery and development is time-consuming and resource-intensive, motivating computational approaches such as diffusion models for de novo drug design.

By Ruoxi Gao, Jiangweizhi Peng, Ziqi Chen, Frazier N. Baker, David C. Kombo, John L. Kane Jr., Andrew A. Scholte, Yi Li, Matthew J. LaMarche, Luigi I. Iconaru, Hans-Peter Biemann, Mingyi Hong, Xia Ning

diffusion benchmarks

arXiv AI

Sep 2

MMAI Gym for Science: Training Liquid Foundation Models for Drug Discovery

arXiv:2603.03517v2 Announce Type: replace-cross Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and perfor...

By Maksim Kuznetsov, Zulfat Miftahutdinov, Rim Shayakhmetov, Mikolaj Mizera, Roman Schutski, Bogdan Zagribelnyy, Ivan Ilin, Nikita Bondarev, Thomas MacDougall, Mathieu Reymond, Mihir Bafna, Kaeli Kaymak-Loveless, Eugene Babin, Maxim Malkov, Mathias Lechner, Ramin Hasani, Alexander Amini, Vladimir Aladinskiy, Alex Aliper, Alex Zhavoronkov

llms benchmarks

arXiv Machine Learning

Jun 9

Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction

arXiv:2604. 26498v3 Announce Type: replace Abstract: The rapid growth of molecular foundation models and large language models (LLMs) has encouraged a scale centred view of AI in drug discovery, in which larger pretrained models are expected to supersede compact cheminformatics models.

By Jinjiang Guo, Sheng Ding

llms benchmarks safety

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