14 papers · ranked by Valyu relevance
Jonas Vester, David Carrasco-Busturia, Kenneth Ruud, Magnus Ringholm + 1 more
We present a workflow, benchmarks, and applications to provide a roadmap for simulating harmonic IR and Raman spectra for large solute-solvent systems by employing a polarizable-embedding quantum-mechanics (PE-QM) approach. This multiscale modeling scheme divides the system into a central core region described by…
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Optimizing the synthesis conditions of advanced materials is challenging, especially when outcomes are subject to inherent experimental uncertainties. Bayesian optimization is a popular tool for accelerating materials discovery, but its standard risk-neutral framework overlooks the variability of outcomes under…
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Physics-based methods such as protein-ligand binding free energy calculations have been increasingly adopted in early-stage drug discovery to prioritize promising compounds for synthesis. However, the accuracy of these methods is highly dependent on details of the calculation and choices made while preparing the…
Thomas Lynn, Julio Ottino, Richard Lueptow, Paul Umbanhowar
Cut-and-shuffle mixing is an instructive candidate system with which to assess the potential of machine learning (ML) as an approach to solve difficult mixing problems. We focus on a specific subset of cut-and-shuffle systems, the one-dimensional interval exchange transform. This class of mixing operations is well…
Zhenghao Wu, Tianhang Zhou
In the realm of multiscale molecular simulations, structure-based coarse graining is a prominent approach for creating efficient coarse-grained (CG) representations of soft matter systems such as polymers. This involves optimizing CG interactions by matching static correlation functions of corresponding degrees of…
Vincent Dufour-Decieux, Brandi Ransom, Rodrigo Freitas, Jose Blanchet + 1 more
Molecular Dynamics (MD) simulations are a key tool to understand the mechanism of complex chemical system and observe their outcomes in different conditions. However, such simulations are computationally expensive, which limits their timescales to the nanoseconds. This limitation is inconsequential at high…
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Accurately predicting the diverse bound-state conformations of small molecules is crucial for successful drug discovery and design, particularly when detailed protein-ligand interactions are unknown. Established tools exist, but efficiently exploring the vast conformational space remains challenging. This work…
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The construction of large benchmark sets has accelerated advancement of quantum chemistry methods, especially in density functional theory and lower-cost methods. However, these large benchmark sets can be unsuitable for cutting-edge method development, because research codes developed for fundamentally new approaches…
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Investigating the molecular structure of soil organic matter (SOM), along with its intramolecular interactions and interactions with other soil components and xenobiotics, is essential due to its ecological importance. However, the complexity and heterogeneity of SOM present significant challenges for systematic…
Kohulan Rajan, Henning Otto Brinkhaus, Achim Zielesny, Christoph Steinbeck
Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information found in traditional laboratory notebooks or for facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for…
Marina Gorostiola González, Olivier J. M. Béquignon, Emma Manners, Anna Gaulton + 7 more
Bioactivity prediction is essential in computational drug discovery, particularly within virtual screening campaigns. Despite advancements in model architectures and features, the sparsity and quality of relevant training data remain a major bottleneck. Notably, genetic variance annotation, crucial for understanding…
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Quantities calculated from molecular simulations are often subject to an initial bias due to unrepresentative starting configurations. Initial data are usually discarded to reduce bias. Chodera's method for automated truncation point selection [J. Chem. Theory Comput. 2016, 12, 4, 1799–1805] is popular but has not been…
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Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…