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Integrating Computation, Experiment, and Machine Learning in the Design of Peptide-Based Supramolecular Materials and Systems

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Angewandte Chemie 2023年第18期135卷

作者： Maithreyi Ramakrishnan Alexander van Teijlingen Tell Tuttle R. V. Ulijn Advanced Science Research Center (ASRC) at the Graduate Center City University of New York (CUNY) New York NY 10031 USA Department of Chemistry Hunter College The City University of New York New York NY 10065 USA Ph.D. Program in Chemistry The Graduate Center of the City University of New York New York NY 10016 USA Pure and Applied Chemistry University of Strathclyde 295 Cathedral Street Glasgow G1 1XL UK Ph.D. Program in Chemistry and PhD program in Biochemistry The Graduate Center of the City University of New York New York NY 10016 USA

Interest in peptide-based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self-assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field.

关键词： Computation Molecular Dynamics Peptides Self-Assembly Supramolecular Chemistry

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Core variables for real-world clinicogenomic data collection in precision oncology

ESMO Real World Data and Digital Oncology

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ESMO Real World Data and Digital Oncology 2025年 7卷 100117-100117页

作者： R. Dienstmann A. Hackshaw J.-Y. Blay C. Le Tourneau OC Precision Medicina Oncoclínicas & Co São Paulo Brazil PhD Program Doctoral School University of Vic—Central University of Catalonia Barcelona Spain Oncology Data Science Vall d’Hebron Institute of Oncology Barcelona Spain Cancer Research UK and UCL Cancer Trials Centre University College London London UK Centre Léon Bérard and Université Claude Bernard Lyon France Institut Curie Department of Drug Development and Innovation (D3i) Paris-Saclay University Paris France

Background Precision oncology evidence gaps may be bridged using real-world clinicogenomic data; however, current limitations compromise real-world data collection and evidence generation. We aimed to define a set of precision oncology core variables that can aid in fit-for-purpose real-world data collection to be used for a range of purposes and by various stakeholders based on the approach adopted when designing the WAYFIND-R registry (NCT04529122). Materials and methods A panel of precision oncology experts created a list of variables based on standard dictionaries and vocabularies, which was aligned with the European Medical Agency’s draft guidelines for registry-based studies. A list of core variables was selected from the initial broad list of variables. Results The initial overall list (∼500 variables) was consolidated to ∼150 variables that covered the entirety of the patient journey in oncology. The panel gave highest priority to patient demographics, socioeconomic information and comorbidities, cancer details, molecular information, particularly predictive biomarkers in routine use and next-generation sequencing technical aspects, systemic cancer therapies and other treatments, outcome assessments and survival outcomes. Conclusions The core oncology variables represent a harmonized list of key clinicogenomic data elements that can be collected during a patient’s journey in oncology and leveraged for their primary intended purpose of molecular tumor board decision making, and might be used for further secondary research purposes, if legal requirements allow. Definition of this list of core variables will help generate evidence from research- or regulatory-oriented precision oncology studies by ensuring synergy, dataset connectivity, and interoperability across different data.

关键词： precision oncology real-world data solid tumors next-generation sequencing molecular tumor boards

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Discovery of Novel Zoanthamine-Type Alkaloids Derived from Cultured Zoanthus Kuroshio with Prominent Therapeutic Potential Against Osteoporosis

SSRN

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SSRN 2025年

作者： Pham, Ngoc-Thac Peng, Bo-Rong Le, Huong-Giang Chen, Yun-Shiuan Huynh, Thanh Hao Chen, Lo-Yun Nguyen, Le Anh Tuan Nguyen, Dang T. Su, Jui-Hsin El-Shazly, Mohamed Lee, Mei-Hsien Lai, Kuei-Hung PhD Program in Clinical Drug Development of Herbal Medicine College of Pharmacy Taipei Medical University Taipei110301 Taiwan Graduate Institute of Pharmacognosy College of Pharmacy Taipei Medical University Taipei110301 Taiwan Graduate Institute of Healthy Industry Technology Research Center for Chinese Herbal Medicine College of Human Ecology Chang Gung University of Science and Technology Taoyuan333324 Taiwan Faculty of Pharmacy Lac Hong University Dongnai Bien Hoa Viet Nam Faculty of Applied Sciences Ton Duc Thang University Ho Chi Minh City Viet Nam National Museum of Marine Biology and Aquarium Pingtung944401 Taiwan Department of Marine Biotechnology and Resources National Sun Yat-sen University Kaohsiung804201 Taiwan Department of Pharmacognosy Faculty of Pharmacy Ain-Shams University Organization of African Unity Street Abassia Cairo11566 Egypt Center for Reproductive Medicine and Sciences Taipei Medical University Hospital Taipei110301 Taiwan Traditional Herbal Medicine Research Center Taipei Medical University Hospital Taipei110301 Taiwan

Marine invertebrates are well-established as prolific sources of secondary metabolites, renowned for their remarkable chemical diversity and extensive spectrum of biological activities. Among these, Zoanthus Kuroshio (ZK) has gained significant attention due to its abundant chemical constituents, particularly alkaloids, which exhibit promising bioactivities against osteoporosis. To investigate its therapeutic potential, the extracts of cultured ZK exhibited significant anti-osteoporotic activity in vitro with MG-63 cells, leading to the integration of Global Natural Products Social Molecular Networking (GNPS) to identify fractions containing the highest amounts of alkaloids. Subsequent isolation efforts resulted in the discovery of five novel zoanthamine-type alkaloids: norzoabenzaldehyde (1), norzoazepanol (2), 11-keto-3-acetoxynorzoanthamine (3), 11-hydroxynorzoanthamide B (4), and 11-hydroxyzoanthamide B (5), alongside four previously identified compounds, including norzoanthaminone (6), 3-acetoxynorzoanthamine (7), 3-acetoxyzoanthamine (8), and 3-hydroxynorzoanthamine (9). Structural elucidation of compounds 1, 3, 4, and 5 was achieved using comprehensive spectroscopic analyses, whereas compound 2 required advanced techniques, including time-dependent density functional theory nuclear magnetic resonance (TDDFT-NMR) calculations and comparative analysis of theoretical and experimental electronic circular dichroism (ECD) data, to ascertain its absolute configuration. Bioassays evaluating alkaline phosphatase activity and mineralization demonstrated that compound 9 exhibited significant activity, with an IC50 value of 20 µM. The mechanism by which this compound operates was elucidated through network pharmacology, molecular docking and dynamics simulations, further confirming the stability of interactions between compound 9 and key protein targets, thereby emphasizing its potential as a promising lead compound for osteoporosis therapeutics. © 2025, The Authors. All

关键词： Spectroscopic analysis

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Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy

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Computer Methods and programs in Biomedicine 2023年 233卷 107480-107480页

作者： Huang, Chih-Wei Wu, Bethany C.Y. Nguyen, Phung Anh Wang, Hsiao-Han Kao, Chih-Chung Lee, Pei-Chen Rahmanti, Annisa Ristya Hsu, Jason C. Yang, Hsuan-Chia Li, Yu-Chuan Jack Taipei Medical University Taipei Taiwan Taipei Medical University Ringgold standard institution - Center for Simulation in Medical Education Taipei 116 Taiwan National Taiwan University Children and Family Research Center Sponsored by CTBC Charity Foundation Taipei Taiwan Clinical Data Center Office of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University TMU Da'an Campus 15 F No. 172-1 Kee lung Road Section 2 Da-an District Taipei Taiwan Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan Department of Dermatology Wanfang Hospital Taipei Medical University Taiwan Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Delta Research Center Taipei Taiwan Department of Health Policy and Management Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia International PhD Program in Biotech and Healthcare Management College of Management Taipei Medical University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan

Background and objective: The promising use of artificial intelligence (AI) to emulate human empathy may help a physician engage with a more empathic doctor-patient relationship. This study demonstrates the application of artificial empathy based on facial emotion recognition to evaluate doctor-patient relationships in clinical practice. Methods: A prospective study used recorded video data of doctor-patient clinical encounters in dermatology outpatient clinics, Taipei Municipal Wanfang Hospital, and Taipei Medical University Hospital collected from March to December 2019. Two cameras recorded the facial expressions of four doctors and 348 adult patients during regular clinical practice. Facial emotion recognition was used to analyze the basic emotions of doctors and patients with a temporal resolution of 1 second. In addition, a physician-patient satisfaction questionnaire was administered after each clinical session, and two standard patients gave impartial feedback to avoid bias. Results: Data from 326 clinical session videos showed that (1) Doctors expressed more emotions than patients (t [326] > = 2.998, p © 2023

关键词： Emotion Recognition

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Application of artificial intelligence in endoscopic image analysis for the diagnosis of a gastric cancer pathogen-Helicobacter pylori infection

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Scientific Reports 2023年第1期13卷 1-12页

作者： Lin, Chih-Hsueh Hsu, Ping-I Tseng, Chin-Dar Chao, Pei-Ju Wu, I-Ting Ghose, Supratip Shih, Chih-An Lee, Shen-Hao Ren, Jia-Hong Shie, Chang-Bih Lee, Tsair-Fwu Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Medical Physics and Informatics Laboratory of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung 80778 Taiwan Division of Gastroenterology Department of Medicine An Nan Hospital China Medical University Tainan Taiwan Department of Education and Research An Nan Hospital China Medical University Tainan Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine Antai Medical Care Corporation Antai Tian-Sheng Memorial Hospital Pingtung County Donggan Taiwan Department of Nursing Meiho University Pingtung County Neipu Taiwan Department of Radiation Oncology Linkou Chang Gung Memorial Hospital and Chang Gung University College of Medicine Linkou Taiwan Department of Medical Imaging and Radiological Sciences Kaohsiung Medical University Kaohsiung 80708 Taiwan PhD Program in Biomedical Engineering Kaohsiung Medical University Kaohsiung 80708 Taiwan School of Dentistry College of Dental Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan

Helicobacter pylori (H. pylori) infection is the principal cause of chronic gastritis, gastric ulcers, duodenal ulcers, and gastric cancer. In clinical practice, diagnosis of H. pylori infection by a gastroenterologists’ impression of endoscopic images is inaccurate and cannot be used for the management of gastrointestinal diseases. The aim of this study was to develop an artificial intelligence classification system for the diagnosis of H. pylori infection by pre-processing endoscopic images and machine learning methods. Endoscopic images of the gastric body and antrum from 302 patients receiving endoscopy with confirmation of H. pylori status by a rapid urease test at An Nan Hospital were obtained for the derivation and validation of an artificial intelligence classification system. The H. pylori status was interpreted as positive or negative by Convolutional Neural Network (CNN) and Concurrent Spatial and Channel Squeeze and Excitation (scSE) network, combined with different classification models for deep learning of gastric images. The comprehensive assessment for H. pylori status by scSE-CatBoost classification models for both body and antrum images from same patients achieved an accuracy of 0.90, sensitivity of 1.00, specificity of 0.81, positive predictive value of 0.82, negative predicted value of 1.00, and area under the curve of 0.88. The data suggest that an artificial intelligence classification model using scSE-CatBoost deep learning for gastric endoscopic images can distinguish H. pylori status with good performance and is useful for the survey or diagnosis of H. pylori infection in clinical practice. © 2023, Springer Nature Limited.

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Influence of Agarose in Semi-Ipn Hydrogels for Sustained Polymyxin B Release

SSRN

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SSRN 2024年

作者： Filho, David Guerrero, Marcelo Castro, Ricardo I. Rafael, Diana Andrade, Fernanda Lara, Adolfo Marican Valdes, Oscar Vargas, Esteban Valenzuela, Elisa Mora, Claudia Durán-Lara, Esteban F. Laboratory of Bio & Nano Materials Drug Delivery and Controlled Release Department of Microbiology Faculty of Health Sciences University of Talca Talca Chile Multidisciplinary Agroindustry Research Laboratory Instituto de Ciencias Aplicadas Facultad de Construcción y Medio Ambiente Universidad Autónoma de Chile Talca Chile Vall d’Hebron University Hospital Vall d’Hebron Barcelona Hospital Campus Passeig de la Vall d’Hebron 119-129 Barcelona08035 Spain Instituto De Salud Carlos III Madrid Spain Barcelona08035 Spain Av. de Joan XXIII 27-31 Barcelona08028 Spain Institute of Chemistry of Natural Research University of Talca Talca3460000 Chile Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule Talca Chile Center for the Development of Nanoscience and Nanotechnology Santiago Chile School of Biochemistry Institute of Biological Sciences University of Talca Talca Chile PhD Program in Science R&D Bioactive Products Department Chemistry Institute of Natural Resources University of Talca Talca Chile

Hydrogels (HGs) are 3-D polymeric networks with high water content, making them appropriate for biomedical applications such as drug delivery systems. This study examines the impact of agarose in semi-interpenetrating polymer networks (Semi-IPNs) based on poly(acrylic acid) (p(AA)), N, N' Methylenebis(acrylamide) (MBA) and agarose (AGA) on the sustained release of Polymyxin B (PolB). Agarose incorporation improved the mechanical strength, swelling behavior and drug retention capacity of the HG. We synthesized the Semi-IPN HGs via free radical polymerization and characterized their structural and thermal properties using Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). The features of swelling under physiological conditions were carried out. Additionally, we conducted release kinetics using the three prepared HGs, each of which had a distinct amount of AGA. The findings demonstrated that the Semi-IPN HGs with greater AGA concentrations had drug release profiles that were slower and more sustained, making them perfect for long-term therapeutic uses. We also tested the PolB-loaded HGs' antimicrobial efficacy against Pseudomonas aeruginosa, and they showed sustained antibacterial activity. Using NIH-3T3 fibroblast cells, we verified the HGs' biocompatibility, demonstrating their appropriateness for use in biomedicine. According to these findings, agarose modified Semi-IPN HGs may find application in long-term medication delivery systems that aid in the treatment of infections and promote wound healing. © 2024, The Authors. All rights reserved.

关键词： Fourier transform infrared spectroscopy

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Variable effects of diphenyl diselenide (PHSE) 2 on mitochondrial pathways according to cellular energy status in bovine vascular endothelial cells

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Free Radical Biology and Medicine 2025年 233卷 S52-S52页

作者： Letícia Selinger Galant Laura Doblado Rafael Radi João Batista Teixeira da Rocha Andreza Fabro de Bem Maria Monsalve Biochemistry PhD Program Federal University of Santa Catarina Florianópolis SC Brazil Instituto de Investigaciones Biomédicas Sols-Morreale (CSIC-UAM) Madrid Spain Center for Free Radical and Biomedical Research (CEINBIO) Facultad de Medicina Universidad de la República Montevideo Uruguay Department of Biochemistry and Molecular Biology Federal University of Santa Maria Santa Maria Brazil Laboratory of Bioenergetics and Metabolism Institute for Biological Sciences； University of Brasília Brasília Brazil National Institute of Science and Technology on Neuroimmunomodulation Rio de Janeiro Brazil

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Microfluidic nanodevices for drug sensing and screening applications

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Biosensors and Bioelectronics 2023年 219卷 114783页

作者： Pal, Arnab Kaswan, Kuldeep Barman, Snigdha Roy Lin, Yu-Zih Chung, Jun-Hsuan Sharma, Manish Kumar Liu, Kuei-Lin Chen, Bo-Huan Wu, Chih-Cheng Lee, Sangmin Choi, Dongwhi Lin, Zong-Hong Institute of Biomedical Engineering National Tsing Hua University Hsinchu30013 Taiwan International Intercollegiate PhD Program National Tsing Hua University Hsinchu30013 Taiwan Department of Power Mechanical Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu30013 Taiwan Department of Chemistry National Tsing Hua University Hsinchu30013 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan Department of Gastroenterology and Hepatology Chang Gung Memorial Hospital Linkou Medical Center Taoyuan333 Taiwan Center of Quality Management National Taiwan University Hospital Hsinchu Branch Hsinchu30059 Taiwan College of Medicine National Taiwan University Taipei10051 Taiwan Institute of Cellular and System Medicine National Health Research Institute Zhunan 35053 Taiwan School of Mechanical Engineering Chung-Ang University Seoul06974 Korea Republic of Kyung Hee University Gyeonggi 17104 Korea Republic of

The outbreak of pandemics (e.g., severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 in 2019), influenza A viruses (H1N1 in 2009), etc.), and worldwide spike in the aging population have created unprecedented urgency for developing new drugs to improve disease treatment. As a result, extensive efforts have been made to design novel techniques for efficient drug monitoring and screening, which form the backbone of drug development. Compared to traditional techniques, microfluidics-based platforms have emerged as promising alternatives for high-throughput drug screening due to their inherent miniaturization characteristics, low sample consumption, integration, and compatibility with diverse analytical strategies. Moreover, the microfluidic-based models utilizing human cells to produce in-vitro biomimetics of the human body pave new ways to predict more accurate drug effects in humans. This review provides a comprehensive summary of different microfluidics-based drug sensing and screening strategies and briefly discusses their advantages. Most importantly, an in-depth outlook of the commonly used detection techniques integrated with microfluidic chips for highly sensitive drug screening is provided. Then, the influence of critical parameters such as sensing materials and microfluidic platform geometries on screening performance is summarized. This review also outlines the recent applications of microfluidic approaches for screening therapeutic and illicit drugs. Moreover, the current challenges and the future perspective of this research field is elaborately highlighted, which we believe will contribute immensely towards significant achievements in all aspects of drug development. © 2022 Elsevier B.V.

关键词： Microfluidics

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Metal ratio and bimetal nanoarchitectonics of ammonia-based fluoride complex induced nickel hydroxide and manganese oxide composites as active materials of an energy storage device

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Journal of Energy Storage 2024年 80卷

作者： Hsu, Chuan-Chih Yu, You Zher Wu, Chung-Hsien Lee, Pin-Yan Chen, Hung-Ming Husain, Sadang Kongvarhodom, Chutima Hsiao, Yu-Cheng Lin, Lu-Yin Division of Cardiovascular Surgery Department of Surgery School of Medicine College of Medicine Taipei Medical University Taiwan Division of Cardiovascular Surgery Department of Surgery Taipei Medical University Hospital Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Gingen Technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei11054 Taiwan Department of Physics Faculty of Mathematics and Natural Science Lambung Mangkurat University Banjarmasin70124 Indonesia Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok10140 Thailand Department of Chemical Engineering Ming Chi University of Technology New Taipei City24301 Taiwan Cell Physiology and Molecular Image Research Center Wan Fang Hospital Taipei Medical University Taipei11696 Taiwan International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Stanford Byers Center for Biodesign Stanford University CA94305-5428 United States Graduate Institute of Biomedical Optomechatronics College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan

Nickel and manganese compounds are widely applied as efficient battery-type active materials for energy storage devices, due to high theoretical capacities and abundant redox states for generating redox reactions to store charges. Morphology plays significant roles on energy storage ability. Structure directing agent (SDA) is widely reported to have ability for modulating configurations of active materials and to achieve excellent surface properties. In this work, it is the first time to use NH4BF4 and NH4HF2 as structure directing agents for synthesizing nickel manganese compounds derived from ZIF67 as active materials of energy storage devices. Bimetallic compounds are composed of nickel hydroxide and manganese oxide, which presents the favorable two-dimensional flower-like structure with high surface area and abundant redox states. The largest specific capacitance (CF) of 1358.2 F/g corresponding to the capacity of 226.4 mAh/g are achieved for the bimetallic compound synthesized using the Ni to Mn precursor ratio of 1 to 1 (NiMn11). The energy storage device assembled using NiMn11 and reduced graphene oxide on Ni foams presents a maximum energy density of 11.45 Wh/kg at the power density of 0.35 kW/kg, and excellent long-term stability with the CF retention of 85.6% and Coulombic efficiency of 87.5% after 10,000 cycles. © 2023

关键词： Fluorine compounds

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OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

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arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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