The study reports the first computational design of de novo miniprotein binders that target the IL‑23 cytokine subunit p19, specifically the IL‑23R‑binding face. Using a length‑robust interface metric (ipSAE) derived from AlphaFold‑predicted aligned error, the authors identified 42 of 50 designs with high interface quality, and five all‑alpha‑helical leads that engage all six targeted hotspots without affecting the p40 subunit. These candidates are proposed as potential neutralizers of IL‑23 activity, offering a smaller, potentially cheaper alternative to existing anti‑p19 antibodies. whyItMatters":"The work introduces a novel computational approach to generate miniprotein candidates that could inhibit IL‑23‑mediated inflammation, addressing a therapeutic need unmet by current large antibody treatments."
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.
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.
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.
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.
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.
SurfSpec is a new framework for lead optimization that improves drug specificity without needing off‑target structures. It works by measuring and reducing the geometric mismatch between a ligand and its target pocket, using the triangle inequality to infer a lower bound on mismatch to off‑target pockets. In tests on the CrossDocked2020 dataset, SurfSpec lowers geometric mismatch and achieves higher empirical specificity while still improving target affinity.
arXiv:2608. 19808v1 Announce Type: new Abstract: Cyclic peptides are emerging as promising molecular scaffolds in drug discovery due to their high binding affinity and structural stability.
SpecOpt is a new molecular design task that optimizes the binding specificity of existing drugs by making constrained structural modifications. The method uses an agentic framework that docks a compound against its intended target and known off‑targets, compares residue‑aware atom‑protein contacts, and feeds the differential interactions to a large language model to propose changes. On a benchmark of 915 compounds, SpecOpt increased the target‑off‑target binding gap for 84.8% of cases while preserving drug‑like properties and structural similarity.
arXiv:2603. 13431v3 Announce Type: replace-cross Abstract: Computational antibody design has seen rapid methodological progress, with dozens of deep generative methods proposed in the past three years, yet the field lacks a standardized benchmark for fair comparison and model development.
arXiv:2606. 00555v1 Announce Type: new Abstract: Structure-based drug design increasingly employs LLM agents to iteratively refine ligands against a target pocket, yet a viable ligand must satisfy two often-conflicting objectives -- binding affinity and druggability -- which single optimization steps rarely improve together.
arXiv:2610.04850v2 Announce Type: replace Abstract: Protein binding prediction is central to target identification, therapeutic binder design, and large scale screening, yet remains challenging becau...