From owner-chemistry@ccl.net Mon Jun 24 12:06:01 2024 From: "Gabriele Mogni gabriele.mogni ~~ gmail.com" To: CCL Subject: CCL: Upcoming Webinar: Modeling structural transitions in condensed matter Message-Id: <-55173-240624120446-26791-BWgncStG3u07qUpEgD9QCQ,server.ccl.net> X-Original-From: "Gabriele Mogni" Date: Mon, 24 Jun 2024 12:04:44 -0400 Sent to CCL by: "Gabriele Mogni" [gabriele.mogni-x-gmail.com] MatSQ Upcoming Webinar | Tue. 9th of July 2024: 2-3 PM CEST We are thrilled to announce another fascinating episode in our Webinar Series at Materials Square, to which you are all invited to participate! Please follow this link for free registration: https://www.materialssquare.com/webinar Webinar Title: "Modeling structural transitions in condensed matter: order parameters and kinetic rates from affordable amounts of simulation data" Presenter: Dr. Fabio Pietrucci (Associate Professor, Sorbonne University) Materials as well as chemical and biological systems display a wealth of structural transitions between metastable states: understanding and predicting these processes in terms of mechanisms and kinetics is a crucial challenge in many disciplines, and computer simulations (in particular molecular dynamics) can be a central tool. Common examples of condensed mater transformations are phase transitions, like the crystallization of a liquid, or a chemical reaction like the breaking of a solvated molecule into two fragments, or the folding and unfolding of a protein. The probabilities of the different states of the system are depicted by a free-energy landscape as a function of the order parameter, and kinetic rates can be extracted from the diffusion properties on such a landscape. I will address two fundamental questions: What is a good order parameter for a given structural transformation? Can we infer, at the same time, the optimal order parameter, the associated free energy landscape and the kinetic rates from limited, affordable amounts of simulation data? I will present basic principles as well as the computational approaches we recently developed to address these questions that circumvent the expensive techniques available today. The webinar will be held according to the following international schedules: Tue, July 9, 2024, 05:00 ~ 06:00 | Los Angeles (PDT) Tue, July 9, 2024, 08:00 ~ 09:00 | New York (EDT) Tue, July 9, 2024, 14:00 ~ 15:00 | Paris (CEST) Tue, July 9, 2024, 15:00 ~ 16:00 | Riyadh (KSA) Tue, July 9, 2024, 17:30 ~ 18:30 | New Delhi (IST) Tue, July 9, 2024, 21:00 ~ 22:00 | Seoul (KST) As always, registration for Materials Square webinars is completely FREE. A participation certificate will also be made available for download to all members of the audience at the end of the event. We look forward to welcoming you virtually! Many thanks for your interest and your consideration, The Virtual Lab Team From owner-chemistry@ccl.net Mon Jun 24 17:16:01 2024 From: "Oleg Trott trott[-]caa.columbia.edu" To: CCL Subject: CCL: AlphaFold 3 vs AutoDock Vina Message-Id: <-55174-240624144831-17261-Xon7r69pDpi2ERfdZnr/yg(0)server.ccl.net> X-Original-From: "Oleg Trott" Date: Mon, 24 Jun 2024 14:48:29 -0400 Sent to CCL by: "Oleg Trott" [trott-$-caa.columbia.edu] Hello, everyone! DeepMind's new AlphaFold 3 attempts to predict protein-ligand binding, and their publication compares it to AutoDock Vina (which I built). But their methodology seems strange. I wrote up my comments in a blog post. If you have an interest in AI and/or docking, I hope you'll find it insightful. https://olegtrott.substack.com/p/are-alphafolds-new-results-a-miracle