Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there is a lack of sufficiently large data collections that include first-principle quantum chemical information on bioactive molecules, such as single-point electronic properties, quantum mechanical wave functions and density-functional theory (DFT) matrices. The open-access QMugs (Quantum-Mechanical Properties of Drug-like Molecules) dataset fills this void. The QMugs collection comprises quantum mechanical properties of more than 665k biologically and pharmacologically relevant molecules extracted from the ChEMBL database, totaling $\sim$2M conformers. QMugs contains optimized molecular geometries and thermodynamic data obtained via the semi-empirical method GFN2-xTB. Atomic and molecular properties (e.g., partial charges, energies, and rotational constants) are provided on both the GFN2-xTB and on the DFT ($ω$B97X-D/def2-SVP) levels of theory. QMugs also comprises the respective quantum mechanical wave functions, including DFT density and orbital matrices, totaling over 7 terabytes of uncompressed data. This dataset is intended to facilitate the development of models that learn from molecular data on different levels of theory while also providing insight into the corresponding relationships between molecular structure and biological activity.
Coupling Quantum Mechanical Modeling and Molecular Dynamics on Heterogeneous Supercomputers for Studying Distal Mutation Effects on Drug Binding in HIV-1
RIKEN Center for Computational Science（理化学研究所计算科学中心）；INRIA（法国国家信息与自动化研究所）；University Grenoble Alpes（格勒诺布尔阿尔卑斯大学）
Ranking the binding of small molecules to protein receptors through physics-based computation remains challenging. Though inroads have been made using free energy methods, these fail when the underlying classical mechanical force fields are insufficient. In principle, a more accurate approach is provided by quantum mechanical density functional theory (DFT) scoring, but even with approximations, this has yet to become practical on drug discovery-relevant timescales and resources. Here, we describe how to overcome this barrier using algorithms for DFT calculations that scale on widely available…
Emergence of antibiotic resistance due to New Delhi Metallo $β$-lactamase (NDM-1) bacterial enzymes is of great concern due to their ability to hydrolyze wide range of antibiotics. Efforts are ongoing to obtain the atomistic details of the hydrolysis mechanism in order to develop novel drugs and inhibitors against NDM-1. Especially, it remains elusive how drug molecules of different family of antibiotics are hydrolyzed by NDM-1 in an efficient manner. Here we report the detailed molecular mechanism of NDM-1 catalyzed hydrolysis of cephalexin, a cephalosporin family drug, and meropenem, a carba…
QUPEX: A Streamlit-Based Framework for Visualizing Quantum-Mechanical Property Spaces of Organic Molecules
Faculty of Computer Science, TUD Dresden University of Technology（德累斯顿工业大学计算机学院）；Center for Advanced Systems Understanding (CASUS)（高级系统理解中心）；Helmholtz Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗斯多夫亥姆霍兹中心）
Artificial micro and nano machines have been envisioned and demonstrated as potential candidates for variety of applications, ranging from targeted drug or gene delivery, cell manipulation, environmental sensing and many more. Here, we demonstrate the application of helical nanomachines that can measure and map the local rheological properties of a complex heterogeneous environment. The position of the helical nanomachine was controlled precisely using magnetic fields, while the instantaneous orientation provided an estimation of the viscosity of the surrounding medium with high spatial and te…
Fragment Molecular Orbital (FMO) calculations provide a framework to speed up quantum mechanical calculations and so can be used to explore structure-energy relationships in large and complex biomolecular systems. These calculations are still onerous, especially when applied to large sets of molecules. Therefore, cyberinfrastructure that provides mechanisms and user interfaces that manage job submissions, failed job resubmissions, data retrieval, and data storage for these calculations are needed. Motivated by the need to rapidly identify drugs that are likely to bind to targets implicated in …
The COVID-19 pandemic has led to unprecedented efforts to identify drugs that can reduce its associated morbidity/mortality rate. Computational chemistry approaches hold the potential for triaging potential candidates far more quickly than their experimental counterparts. These methods have been widely used to search for small molecules that can inhibit critical proteins involved in the SARS-CoV-2 replication cycle. An important target is the SARS-CoV-2 main protease Mpro, an enzyme that cleaves the viral polyproteins into individual proteins required for viral replication and transcription. U…
Google Research（谷歌研究院）；Technische Universität Berlin（柏林工业大学）；University of Luxembourg（卢森堡大学）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical calculations, being practically limited to short timescales and few atoms. For larger systems, efficient, but much less reliable empirical force fields are used. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations, offering similar accuracy as ab initio methods at orders-of-magnitude speedup. Until now, MLFFs mainly capture short-range interactions in small molecules…
University of Michigan（密歇根大学）；Stanford University（斯坦福大学）
Deep learning based methods have been widely applied to predict various kinds of molecular properties in the pharmaceutical industry with increasingly more success. Solvation free energy is an important index in the field of organic synthesis, medicinal chemistry, drug delivery, and biological processes. However, accurate solvation free energy determination is a time-consuming experimental process. Furthermore, it could be useful to assess solvation free energy in the absence of a physical sample. In this study, we propose two novel models for the problem of free solvation energy predictions, …
QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
TUD Dresden University of Technology（德累斯顿工业大学）；Pfizer Worldwide R&D（辉瑞全球研发中心）；Center for Advanced Systems Understanding (CASUS)（高级系统理解中心（CASUS））；Helmholtz Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）
Q-Score: A Quantum-Native Scoring Function for Molecular Docking
Department of Computer Science（计算机科学系）；The Chinese University of Hong Kong（香港中文大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Rutgers University（罗格斯大学）；The University of Chicago（芝加哥大学）
