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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.

Pascal Sturmfels, Naozumi Hiranuma, Milad 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...

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.

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. · 3 citations
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 dock...

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

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