The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the ...results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.
CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with ...generating computational models for the three‐dimensional structure of the corresponding protein–ligand complex. These models were subsequently compared with experimental structures determined by x‐ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding‐Site Superposed, Symmetry‐Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein–Ligand Interactions (lDDT‐PLI) assessed the ability of models to reproduce the protein–ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single‐atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein–ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.
There is no doubt that papers published in the Journal of Chemical Information and Modeling, and related journals, provide valuable scientific information. However, it is often difficult to reproduce ...the work described in molecular modeling and cheminformatics papers. In many cases the software described in the paper is not readily available, in other cases the supporting information is not provided in an accessible format. To date, the major journals in the fields of molecular modeling and cheminformatics have not established guidelines for reproducible research. This letter provides an overview of the reproducibility challenges facing our field and suggests some guidelines for improving the reproducibility of published work.
The basic goal of small-molecule screening is the identification of chemically 'interesting' starting points for elaboration towards a drug. A number of innovative approaches for pursuing this goal ...have evolved, and the right approach is dictated by the target class being pursued and the capabilities of the organization involved. A recent trend in high-throughput screening has been to place less emphasis on the number of data points that can be produced, and to focus instead on the quality of the data obtained. Several computational and technological advances have aided in the selection of compounds for screening and widened the variety of assay formats available for screening. The effect on the efficiency of the screening process is discussed.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Optimizing active learning for free energy calculations Thompson, James; Walters, W Patrick; Feng, Jianwen A ...
Artificial intelligence in the life sciences,
December 2022, 2022-12-00, 2022-12-01, Letnik:
2
Journal Article
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While Relative Binding Free Energy (RBFE) calculations have become a mainstay in lead optimization programs, the computational expense of performing these calculations has limited their broader ...application. Active learning (AL), a machine learning method used to direct a search iteratively, has explored larger chemical libraries using RBFE calculations. While AL has been successfully applied, there has not been a systematic study of the impact of parameter settings on the performance of AL. To address this gap, we have generated an exhaustive dataset of RBFE calculations on 10,000 congeneric molecules. We used this dataset to explore the impact of several AL design choices, including the number of molecules sampled at each iteration, the method used to select an initial sample, the method used to build a machine learning model, and the acquisition function that defines the balance between exploration and exploitation in the search. Our studies demonstrated that the performance of AL is largely insensitive to the specific machine learning method and acquisition functions used. In our studies, the most significant factor impacting performance was the number of molecules sampled at each iteration where selecting too few molecules hurts performance. Under the best conditions, we were able to identify 75% of the 100 top scoring molecules by sampling only 6% of the dataset. We hope that the dataset of 10K molecules will provide the basis for future studies exploring additional AL strategies. The source code and supporting data for the work are available at https://github.com/google-research/google-research/tree/master/al_for_fep.
Molecular modelers and informaticians have the unique opportunity to integrate cross-functional data using a myriad of tools, methods and visuals to generate information. Using their drug discovery ...expertise, information is transformed to knowledge that impacts drug discovery. These insights are often times formulated locally and then applied more broadly, which influence the discovery of new medicines. This is particularly true in an organization where the members are exposed to projects throughout an organization, such as in the case of the global Modeling & Informatics group at Vertex Pharmaceuticals. From its inception, Vertex has been a leader in the development and use of computational methods for drug discovery. In this paper, we describe the Modeling & Informatics group at Vertex and the underlying philosophy, which has driven this team to sustain impact on the discovery of first-in-class transformative medicines.
Prediction of ‘drug-likeness’ Walters, W.Patrick; Murcko, Mark A
Advanced drug delivery reviews,
03/2002, Letnik:
54, Številka:
3
Journal Article
Recenzirano
Recent developments in combinatorial chemistry and high-throughput screening have dramatically increased the scale on which drug discovery programs are carried out. Along with these advances has come ...a need for automated methods of determining which compounds from a library should be synthesized and screened. These methods range from simple counting schemes to sophisticated machine learning techniques such as neural networks. While many of these methods have performed well in validation studies, the field is still in its formative stage. This paper reviews a number of computational techniques for identifying drug-like molecules and examines challenges facing the field.
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