13 papers · ranked by Valyu relevance
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This paper presents GLAS (Git-based Lab Automated Scheduler or Get Lab Automation Simplified), an open-source, robust, and highly expandable Git-based architecture designed for laboratory automation. GLAS can be deployed in both partially and fully automated experimental science laboratories, enabling the development…
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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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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…
Alexander S. Shved, Blake E. Ocampo, Elena S. Burlova, Casey L. Olen + 2 more
The construction, management and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, "molli", which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries.…
Peter Kraus, Edan Bainglass, Francisco F. Ramirez, Enea Svaluto-Ferro + 7 more
Compliance with good research data management practices means trust in the integrity of the data, and it is achievable by a full control of the data gathering process. In this work, we demonstrate tooling which bridges these two aspects, and illustrate its use in a case study of automated battery cycling. We…
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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…
Connor Taylor, Kobi Felton, Daniel Wigh, Mohammed Jeraal + 4 more
Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations need to be executed in the presence of polar functionality necessary for fragment-protein binding. New technologies such as high-throughput experimentation and…
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The integration of artificial intelligence technologies into pharmaceutical research is crucial for gaining an early understanding of molecular properties, thereby facilitating successful drug design. Constructing a machine learning (ML) model however, requires knowledge spanning from data preprocessing and feature…
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The python package ArchOnML ("Archive-On-Machine-Learning") is introduced, which can perform virtual screening projects covering up to millions of structural derivatives through the use of Kernel Ridge Regression models. It supports the full workflow of setting up calculation inputs for external quantum chemistry…
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Accurate prediction of ADME (Absorption, Distribution, Metabolism, and Excretion) properties is a key challenge in drug discovery. In the Polaris Antiviral ADME Prediction Challenge, we developed and benchmarked multi-task directed message passing neural network (D-MPNN) models using ChemProp, trained exclusively on a…
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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…
Pablo Quijano Velasco, Kedar Hippalgaonkar, Balamurugan Ramalingam
The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one…
Alejandro Santana-Bonilla, Raquel Lopez-Rios De Castro, Peike Sun, Robert Ziolek + 1 more
Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale however building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modelling workflows are therefore becoming essential tools in…