14 papers · ranked by Valyu relevance
Authors not listed
Bayesian optimization (BO) has become increasingly important for experimental optimization across scientific domains, yet implementing BO pipelines requires significant programming expertise and familiarity with specialized frameworks. This creates a barrier for domain experts who could benefit from BO but lack the…
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With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies…
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Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and…
Thomas Liphardt, Thomas Sander
We present an efficient algorithm for substructure search in combinatorial libraries defined by synthons, i.e. substructures with connection points. Our method improves on existing approaches by introducing powerful heuristics and fast fingerprint screening to quickly eliminate branches of non matching combinations of…
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Protein conformational landscapes contain the functionally relevant information useful for understanding biological processes. Mapping out conformational landscapes provides valuable insights into protein behaviors and biological phenomena, and has relevance to therapeutic design. While experimental structural biology…
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We report the development and application of a scalable machine learning optimisation framework for batched multi-objective reaction optimisation. Through experimental data-derived benchmarks, we demonstrate our approach’s capacity to efficiently handle large parallel batches and high-dimensional search spaces…
Ian Knight, Khanh Tang, John Irwin
Molecular docking is a widely used technique for leveraging protein structure in ligand discovery, but as a method, it remains difficult to utilize due to limitations that have not been adequately addressed. Despite some progress towards automation, docking still requires expert guidance, hindering its adoption by a…
Ido Ben-Shalom, Charles Lin, Brian Radak, Woody Sherman + 1 more
Molecular dynamics (MD) simulations of proteins are commonly used to sample from the Boltzmann distribution of conformational states, with wide-ranging applications spanning chemistry, biophysics, and drug discovery. However, MD can be inefficient at equilibrating water occupancy for buried cavities in proteins that…
Qi Zhang, Chang Liu, Stephen Wu, Ryo Yoshida
In the last few years, de novo molecular design using machine learning has made great technical progress but its practical deployment has not been as successful. This is mostly owing to the cost and technical difficulty of synthesizing such computationally designed molecules. To overcome such barriers, various methods…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Yann Garniron, Thomas Applencourt, Kevin Gasperich, Anouar Benali + 15 more
Quantum Package is an open-source programming environment for quantum chemistry specially designed for wave function methods. Its main goal is the development of determinant-driven selected configuration interaction (sCI) methods and multi-reference second-order perturbation theory (PT2). The determinant-driven…
Justin Villard, Murat Kılıç, Ursula Rothlisberger
Identification of the most stable structure(s) of a system is a prerequisite for the calculation of any of its properties from first-principles. However, even for relatively small molecules, exhaustive explorations of the potential energy surface (PES) are severely hampered by the dimensionality bottleneck. In this…
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Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…