11 papers · ranked by Valyu relevance
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We report a new charge model and a new general small molecule force field. Here, we address the development and benchmarking of both the Open Force Field (OpenFF) AshGC charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules. AshGC is a graph neural network-based method for efficient…
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Accurately measuring compound binding affinities is key to driving the pharmaceutical development process. Rigorous physics-based in silico approaches, particularly alchemical free energy methods, have become a gold standard tool for estimating compound affinity changes. Here we present the results of a large-scale…
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The analysis of molecular dynamics (MD) simulations is a critical but fragmented process, often requiring researchers to chain together multiple software tools and write bespoke scripts for routine structural and dynamic analyses. This workflow complexity creates a significant barrier to efficiency, standardization…
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Large Language Models have demonstrated impressive capabilities in natural language understanding and processing. However, as AI and LLMs continue to evolve, their ability to accurately and efficiently interpret data from scientific figures and plots remains obscure. In this study, we test and evaluate the ability of…
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The initial formation of secondary aerosols, a large cause of uncertainty in modern radiative forcing modeling, can be simulated using quantum chemical methods. When based on quantum chemistry, the simulations have an exponential dependence on the free energy, requiring a high-accuracy description. In this study, we…
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Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional…
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The reaction H₂ + OH → H₂O + H is fundamental to hydrogen combustion, atmospheric chemistry, and energy systems. Despite numerous experimental studies, comprehensive statistical comparison with modern uncertainty quantification has been lacking. This study presents a systematic analysis of ten independent kinetic…
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Fragment-based drug discovery (FBDD) is a widely used strategy in early-stage drug development, but accurately predicting the binding affinities of fragments and their elaborated analogs poses unique computational challenges. These difficulties arise from weak binding affinities, diverse chemical scaffolds, and limited…
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Developing accurate coarse-grained (CG) molecular models is crucial for building databases of complex chemical molecules and conducting related data-driven research. The accuracy of CG models is typically affected by CG bead resolution and non-bonded interaction parameters. In this study, an optimized Lennard-Jones…
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The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
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Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…