Alivexis Announces Publication of ModBind_dG™ Simulation Technology Paper in PNAS

Alivexis, Inc. (Headquarters: Minato-ku, Tokyo; CEO: Shun Kimura; hereinafter referred to as "the Company") is pleased to announce the publication of a research paper on ModBind_dG™, the latest version of its computational drug discovery platform. The study, titled "ModBind_dG: a simulation-based absolute predictor of free energy of binding based on population reweighting," authored by William Sinko, Head of Computational Chemistry at Alivexis, and colleagues, has been published in the Proceedings of the National Academy of Sciences of the United States of America (PNAS). The paper presents the methodology, validation results, and applications of ModBind_dG™, the Company’s innovative simulation technology. ModBind_dG™ enables rapid and accurate prediction of ligand binding affinity as absolute binding free energy—without relying on reference compound data—and achieves speeds up to thousands of times faster than existing simulation-based methods. Alivexis has developed ModBind_dG™ from fundamental theory and refined the methodology to enable accurate large-scale applications, further expanding the potential of computation-driven drug discovery. ModBind_dG™ is widely used to accelerate the Company’s drug discovery research and has supported collaborative projects with pharmaceutical companies as well as the advancement of multiple clinical candidate compounds within its internal pipeline. Figure: ModBind_dG™ is a novel free energy method for predicting the binding strength of compounds against drug targets. Accelerated binding and unbinding simulations rapidly compute the data required for binding strength prediction. The equations in the figure illustrate the fundamental principles of this method. Title: ModBind_dG: a simulation-based absolute predictor of free energy of binding based on population reweighting Authors: William Sinko, Blake Mertz, Yoh Terada, and S. Roy Kimura (Alivexis, Inc.) Journal: Proceedings of the National Academy of Sciences of the United States of America (PNAS) DOI: 10.1073/pnas.2513285123 Publication Date: June 15, 2026 (Online, Eastern Time, USA) [Comment from Shun Kimura, CEO of Alivexis, Inc.] "We are delighted to announce the publication of a research paper on ModBind_dG™, the latest version of our core computational drug discovery platform. This publication represents a critical milestone, demonstrating the scientific foundation of a technology that has already contributed to our internal drug discovery pipeline and collaborative research with pharmaceutical companies. By enabling rapid and accurate absolute prediction of binding free energy, ModBind_dG™ strengthens our ability to generate high-quality small-molecule drug candidates and create value through strategic partnerships." [Comment from William Sinko, Head of Computational Chemistry at Alivexis, Inc. (U.S. subsidiary)] "ModBind_dG™ predicts ligand binding affinity by estimating absolute binding free energy using a rigorous simulation-based methodology. By simulating the physical dissociation process of molecules under accelerated sampling conditions and determining the ratio of populations in bound and unbound states, the method directly calculates absolute binding free energy. Through optimization of this theoretical framework and simulation protocols, we have opened a path to accurate free energy prediction with dramatically reduced computation time. We believe this approach can support compound evaluation in early-stage drug discovery, from hit identification to lead optimization." About Alivexis, Inc. Company Name: Alivexis, Inc. Headquarters: 7F, Dai-Ichihibiya Building, 1-18-21 Shinbashi, Minato-ku, Tokyo Leadership: CEO Shun Kimura, COO Kazuki Ohno Founded: August 8, 2016 URL: https://alivexis.com Business: A network-based drug discovery company leveraging cutting-edge drug discovery technologies and AI