arXiv:2609.37555v1 Announce Type: new
Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical st...
By Noel Suarez-Barro, Manuel Lama, Juan C. Vidal
The paper introduces NSA-Bench, a public benchmark for predicting nano self‑assembly (NSA) between molecular pairs, framing it as a binary classification problem. It presents NSA‑Net, a multimodal learning framework that fuses graph topology, sequence semantics, and physicochemical descriptors to predict self‑assembly, achieving high ROC‑AUC scores and outperforming existing baselines. The study also demonstrates how NSA‑Net’s predictions can guide experimental formulation refinement through an NSA‑Agent case study.
By Quan Hao, Mengyue Fan, Zifan Dong, Jianduo Zhao, Changhao Xiao, Shangqing Jiao, Hao Zhang, Yudong Wang, Fei Xia, Jigang Wang, Liguo Zhang, Chong Qiu
The study presents an interpretable machine‑learning framework that predicts whether lipid nanoparticles (LNPs) accumulate in the liver or in extrahepatic tissues after intravenous injection. Using a curated dataset of 476 LNP formulations, the authors engineered 808 features from lipid chemistry and formulation composition, and achieved ROC‑AUC scores up to 0.874 with tree‑based models. SHAP analysis identified ionizable‑lipid descriptors and formulation fractions—especially ionizable lipid, sterol, and PEGylated/polymer‑conjugated lipid components—as key drivers of biodistribution, offering actionable design principles for targeting tissues beyond the liver.
By Asal Mehradfar, Mohammad Shahab Sepehri, Owen Antholine, Varun Shankar, Glen S. Kwon, Salman Avestimehr, Morteza Rasoulianboroujeni
arXiv:2504. 13853v2 Announce Type: replace-cross Abstract: Rational design of lipid nanoparticles (LNPs) for tissue-specific delivery critically depends on predicting the composition of the protein corona that forms on the lipid surface after intravenous administration.
By Pingfei Zhu, Hongyi Liu, Xueyan Liu, Zhenjun Yang, Bo Yang
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
arXiv:2606. 30170v1 Announce Type: cross Abstract: Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets.
By Matthias Blaschke, Daniel Kienzle, Zsuzsanna Koczor-Benda, Julian Lorenz, Rainer Lienhart, Fabian Pauly