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