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
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.
By AkshatKumar Nigam, Neri Amara, Hillary Dequina, Dyana Kenanova, Seth Malmersjo, Fiorella Ruggiu, Raymond V. Fucini, Jack Sadowsky, Stig K. Hansen, Imran S. Haque
arXiv:2608. 12219v1 Announce Type: new Abstract: Treating patients with combinations of drugs reduces the risk of resistance to any individual drug.
By Antoine de Mathelin, Christopher Tosh, Wesley Tansey
arXiv:2608. 07609v1 Announce Type: cross Abstract: High-throughput screening (HTS) assays are central to early-stage drug discovery but are often limited by extreme data sparsity, as primary screens typically use only a single replicate per test substance.
By Xiaohua Douglas Zhang
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
The paper investigates how the phrasing of prompts affects large language models (LLMs) in predicting drug toxicity. By varying job role, prompt structure, and rule interpretation, the authors found that natural variability in LLM outputs outweighs fine‑tuning of prompts. However, incorporating chemoinformatic code to extract features significantly improved model performance, suggesting that prompt engineering alone is insufficient for reliable toxicity prediction.
By Mia MacGregor, Aakash Welgamage Don, Mark Bartlett
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
arXiv:2610.07079v1 Announce Type: new
Abstract: Accurate bioactivity prediction is a central challenge in early-stage drug discovery, as individual assays often contain too few measurements to train...
By Michal Kmicikiewicz, Tommy Rochussen, Vincent Fortuin, Ewa Szczurek
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
arXiv:2607. 06605v1 Announce Type: new Abstract: Conformal prediction is being adopted in drug discovery to put an honest number on model reliability: pick an error rate alpha, and the method returns prediction sets containing the true label with probability at least 1 - alpha.
By Muhammadjon Tursunbadalov (School of Science and Technology, Champions College Prep, United States), Mustafojon Tursunbadalov (School of Science and Technology, Champions College Prep, United States)
The paper introduces Temporal Difference Active Learning (TDAL), a method that learns an acquisition function online for perturbation discovery by treating the problem as a sequential decision task. TDAL uses temporal difference learning with the Hellinger distance to weight future value and employs leave‑batch‑out to create virtual transitions, thereby addressing data scarcity. Evaluated on eight public screens involving single, double, and triple perturbations, TDAL outperforms eleven baseline methods, achieving an average rank of 1.63 and topping four tasks.
By Xudong Zhang, Jianzhe Li, Yitong Tang, Peijie Zhou
arXiv:2510.11726v3 Announce Type: replace-cross
Abstract: Generating drug-induced transcriptional responses requires a model to account for cellular background together with molecular identity and do...
By Zhaokang Liang, Shuyang Zhuang, Xiaoran Jiao, Weian Mao, Hao Chen, Chunhua Shen