Skip to content
Log in

Similar papers

Preprint Jul 2026

Accurate structural modeling of chemically diverse molecular interfaces with Vilya-2

Vilya-2 is the structure-prediction oracle that de novo peptide design pipelines require--establishing the all-atom approach as a general foundation for the design and evaluation of de novo peptide therapeutics.

Vilya Research Pascal Sturmfels, Naozumi Hiranuma, M. Salem et al. · 0 citations
2026

Open-Sourced In Silico Drug Screening.

This chapter describes a structure-based computational approach to perform high-throughput ligand screens of chemical libraries using open-source software programs and illustrates this workflow with the enzymatic molecular target NAD(P)H:quinone oxidoreductase1 (NQO1), which is overexpressed in a number of human solid tumors.

Audrey G. Fikes, Melissa C. Srougi · 0 citations
Open access Jul 2026

AI-assisted fragment-based drug discovery of SARS-CoV-2 macrodomain binders validated by NMR and X-ray crystallography.

Fragment-based drug discovery (FBDD) is an effective approach for exploring chemical space using small, low-affinity fragments as starting points to facilitate development of lead compounds. Strategies to improve fragment potency include fragment merging and linking to generate higher-affinity inhibitors. Recently, artificial intelligence (AI) and machine learning (ML) have accelerated this process through structure-based optimization and generative compound design. Here, we present an AI-assisted FBDD workflow applied to the SARS-CoV-2 macrodomain (Mac1), a conserved viral protein involved in immune evasion and ADP-ribose metabolism. Using available structural data and previously identified fragments, we combined deep learning with molecular docking to design novel Mac1 binders. Selected compounds were synthesized and validated by NMR spectroscopy and X-ray crystallography, demonstrating improved binding relative to the original fragment hits with KD values in the range of 299-990 µM. This study demonstrates the advantages of integrating AI with FBDD to streamline molecular design, providing a data-driven framework for discovering new Mac1 inhibitors and guiding future antiviral drug development.

Elnaz Aledavood, Sandra Ramos-Inza, Jannis Born et al. · 0 citations
Open access Jul 2026

Reliability of AI Methods in Drug Discovery: Evaluation of Boltz‑2 for Structure and Binding Affinity Prediction

An extensive evaluation of Boltz-2 using two large-scale data sets shows that Boltz-2 lacks the energetic resolution required for lead identification, highlighting the necessity of employing physics-based methods for the reliability and refinement of AI-derived models.

S. Wan, Xibei Zhang, Xiao Xue et al. · 1 citation
Jul 2026

Bridging between Structure-Based and Data-Driven Affinity Prediction.

This work introduces a method to smoothly transition from physics-based to knowledge-based predictions based on the uncertainty of each model and shows that combining structure-based and ML models significantly improves the prediction accuracy if training data is limited, whereas the weighting smoothly shifts from docking to ML as more data is acquired.

Ažbeta Kubincová, David L. Mobley · 1 citation
Review 2026

AI-Driven Protein Research: From Prediction to Design.

This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.

Guodong Min, Huan Peng · 0 citations