arXiv:2607. 19237v1 Announce Type: new Abstract: Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics.
By Yiming Qin, Kai Yi, Miruna Cretu, Sjors H. W. Scheres, Pietro Li\`o, Pascal Frossard
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.
Mirror-Score is a calibrated, inference‑only scoring framework that evaluates heterochiral D‑peptide/L‑protein complexes, addressing the inadequacies of raw ProteinMPNN negative log‑likelihood (NLL) ranking. The authors benchmarked 31 crystal complexes across four target families, showing that NLL has poor affinity correlation (Spearman 0.19) while Boltz‑2 mirror‑space co‑folding confidence and pLDDT metrics can correctly rank peptides within a viral‑entry family. The study also proposes a prospective design protocol for antimicrobial‑resistance targets and releases all code, data, and structures publicly.
By Jiada Li
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
CaliPPer is a post‑hoc framework that calibrates and predicts the performance of binding‑prediction models by combining a multi‑chain Sample‑to‑Domain Distance (S2DD) metric with distance‑aware Bayesian recalibration. It operates at three resolutions—generalisability score, aggregate performance prediction, and per‑sample confidence—achieving strong distance‑performance correlations (|r| = 0.80–0.92) and low prediction errors for AUROC, AP, and F1. In retrospective analyses of five published studies, CaliPPer increased true discovery rates, improving AUROC by up to +0.20 on unseen epitopes and variants and raising confirmed neoantigen findings from 0/5 to 3/5.
By Jian-Qing Zheng, Hantao Lou, Zinan Yin, Sam Farrar, Yuze Zhou, Elie Antoun, Xiangxi Wang, Xuetao Cao, Tao Dong
ZetaDial introduces a post‑sampling, per‑protein secant controller that adjusts the net charge of protein binders generated by fixed‑backbone ProteinMPNN, enabling inference‑time tuning of developability‑relevant charge. On benchmark datasets, the secant loop reduced mean absolute charge error compared to fixed‑slope and global‑bias approaches, achieving significant improvements on RCSB complexes and comparable performance on Cas13 monomers. The method also highlights trade‑offs between charge calibration and foldability, with exploratory tests on PD‑L1, IL‑7R‑alpha, and RBD designs illustrating its impact on design success rates and interface properties.
By Mohammed Sameer Syed, Tamara Dinneen