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:2608.29939v1 Announce Type: cross
Abstract: Streaming-potential-mediated transport of viscoelastic fluids has attracted research attention owing to its applications in electrokinetic energy con...
By Ankan Basu, Sumanta Banerjee
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
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.
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:2507. 03209v2 Announce Type: replace-cross Abstract: The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a major bottleneck in RNA therapeutics development.
By Asal Mehradfar, Mohammad Shahab Sepehri, Jose Miguel Hernandez-Lobato, Glen S. Kwon, Mahdi Soltanolkotabi, Salman Avestimehr, Morteza Rasoulianboroujeni
arXiv:2608. 01007v1 Announce Type: new Abstract: Dual-target drug design aims to generate 3D molecules that can simultaneously interact with two target proteins, offering a promising route for discovering polypharmacological compounds against complex diseases.
By Jingyuan Zhou, Shikui Tu, Lei Xu
PocketVE is a protein-pocket-conditioned variance‑exploding diffusion framework that integrates stable 3D coordinate denoising, classifier‑free property guidance, and adaptive protein perturbation. It improves 3D validity from 58.6% to 80.6% and reduces strain energy from 457.4 to 127.9 on CrossDocked2020 while maintaining competitive docking and property scores. The study shows moderate guidance balances target objectives with geometric quality, and diagnostics confirm enhanced pocket compatibility.
By Peining Zhang, Jinbo Bi
The paper introduces Andromeda 2, an agentic system that uses structured in‑house experimental evidence to design and run successive batches of self‑emulsifying drug delivery systems (SEDDS). In a miniaturized automated lab, Andromeda 2 outperformed its predecessor Andromeda 1 and a traditional design‑of‑experiments campaign in developing paclitaxel formulations, achieving a 50 % hit rate versus 17 % and 2 % respectively, and identifying 12 formulations meeting all target product profile objectives. The system’s use of evidence‑grounded reasoning increased mean AUC by 34 % and produced a formulation with a 19 % w/w paclitaxel loading, roughly 3.3‑fold higher than a published benchmark.
By Michael M. Craig, Riley J. Hickman, Yingshan Ma, R\'emi Pich\'e-Taillefer, Christine Allen, Pauric Bannigan
arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.
By Riccardo De Santi, Bruce Lee, Cristian Perez Jensen, Kimon Protopapas, Sophia Tang, Cheng-Hao Liu, Pranam Chatterjee, Yisong Yue, Andreas Krause
arXiv:2606. 05200v1 Announce Type: cross Abstract: Lipid nanoparticles (LNPs) are efficient delivery systems for negatively charged nucleic acids.
By Maria B{\aa}nkestad, Sandra Barman, Magnus R\"oding, Erik Kaunisto, Viktoriia Meklesh, Audrey Gallud, Marco Mendez, Marianna Yanez Arteta, Stefan Norberg, Ann Terry, Smita Chakraborty, Shun Yu, Jerk R\"onnols, Sepideh Pashami
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