AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens

Stafford, Kate A.; Anderson, Brandon; Sorenson, Jon M.; Bedem, Henry van den

doi:10.1021/acs.jcim.1c01250

Cited by 33 publications

(30 citation statements)

References 60 publications

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“…Docking results will differ depending on the conformation, apo/holo status, and quality of structure. One method, screening performance index, can be used to select good structures to use in prospective VS [ 149 ]. This index consists of five calculated terms that describe the docking performance of a set of structures on a set of known active compounds.…”

Section: Structure-based Virtual Screeningmentioning

confidence: 99%

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Protein–Ligand Docking in the Machine-Learning Era

2022

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Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein–ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein–ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.

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Section: Structure-based Virtual Screeningmentioning

confidence: 99%

“…ML can also be applied to this task. Stafford and co-workers introduced AtomNet PoseRanker, a graph CNN trained on PDBbind v2019 to rerank putative co-crystal poses [ 149 ].…”

Section: Structure-based Virtual Screeningmentioning

confidence: 99%

Protein–Ligand Docking in the Machine-Learning Era

2022

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“…Recently, Stafford et al developed a method to score a collection of structures based on the docking performance using a set of known active effectors, and the top ranked structures are amenable for use in VS campaigns [67]. The method is limited to targets that have known effector datasets, of which these data are not available for many targets.…”

Section: Computational Approaches Based On Structural Data: Virtual S...mentioning

confidence: 99%

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

et al. 2022

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The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

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“…On the other hand, SpaceMACS [45] and SpaceLight [4] can provide query-based access to modular libraries by decomposing the query into fragments, and matching those by similarity search to synthons in the library. In parallel to these efforts, machine learning has received significant attention in vHTS: for predicting activity scores given docked conformations [15,43,57], predicting activity scores given a ligand and protein separately (undocked) [38,56], and in improving or altogether replacing classical molecular docking with machine learning approaches [39,51,52].…”

Section: Related Workmentioning

confidence: 99%

An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

Pedawi¹,

Gniewek²,

Chang³

et al. 2022

Preprint

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Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets. However, they are quickly approaching a size beyond that which permits explicit enumeration, presenting new challenges for virtual screening. To overcome these challenges, we propose the Combinatorial Synthesis Library Variational Auto-Encoder (CSLVAE). The proposed generative model represents such libraries as a differentiable, hierarchically-organized database. Given a compound from the library, the molecular encoder constructs a query for retrieval, which is utilized by the molecular decoder to reconstruct the compound by first decoding its chemical reaction and subsequently decoding its reactants. Our design minimizes autoregression in the decoder, facilitating the generation of large, valid molecular graphs. Our method performs fast and parallel batch inference for ultra-large synthesis libraries, enabling a number of important applications in early-stage drug discovery. Compounds proposed by our method are guaranteed to be in the library, and thus synthetically and cost-effectively accessible. Importantly, CSLVAE can encode out-of-library compounds and search for in-library analogues. In experiments, we demonstrate the capabilities of the proposed method in the navigation of massive combinatorial synthesis libraries.

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AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens

Cited by 33 publications

References 60 publications

Protein–Ligand Docking in the Machine-Learning Era

Protein–Ligand Docking in the Machine-Learning Era

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

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