4.7 Article

Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Editorial Material Biochemical Research Methods

Protein structure predictions to atomic accuracy with AlphaFold

John Jumper et al.

NATURE METHODS (2022)

Article Chemistry, Medicinal

Discovery and Preclinical Profiling of GSK3839919, a Potent HIV-1 Allosteric Integrase Inhibitor

Kyle Parcella et al.

Summary: This study focuses on strategic modifications of allosteric HIV-1 integrase inhibitors (ALLINIs), which led to the identification of compounds with enhanced inhibitory potency and pharmacokinetic properties. The researchers optimized the aryl substitutions and discovered a suitable spacer element, resulting in the development of compounds 12 and 22 with strong inhibitory effects and favorable pharmacokinetic profiles.

ACS MEDICINAL CHEMISTRY LETTERS (2022)

Article Biochemistry & Molecular Biology

NeuralDock: Rapid and Conformation-Agnostic Docking of Small Molecules

Congzhou M. Sha et al.

Summary: Virtual screening is a cost- and time-effective alternative to traditional high-throughput screening in the drug discovery process. NeuralDock, a neural network framework, accelerates computational docking and accurately predicts binding energy and affinity of protein-small molecule pairs without prior knowledge of a ligand. It has the potential to be useful in brute-force virtual screening and drug model training.

FRONTIERS IN MOLECULAR BIOSCIENCES (2022)

Article Chemistry, Physical

A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pKa Predictions in Proteins

Pedro B. P. S. Reis et al.

Summary: In this study, deep learning models trained on a dataset of 6 million theoretically determined pK(a) shifts successfully inferred the electrostatic contributions of different chemical groups and the importance of solvent exposure. The models demonstrated the best accuracy in a test set and significantly outperformed physics-based methods in terms of inference speed.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2022)

Article Chemistry, Medicinal

Identification of Cryptic Binding Sites Using MixMD with Standard and Accelerated Molecular Dynamics

Richard D. Smith et al.

Summary: Protein dynamics are crucial in small molecule binding, and the MixMD protocol is effective in identifying cryptic binding sites. However, for proteins requiring significant structural rearrangement, even accelerated dynamics may fail to completely map the binding site.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2021)

Article Chemistry, Physical

TorchMD: A Deep Learning Framework for Molecular Simulations

Stefan Doerr et al.

Summary: TorchMD is a molecular simulation framework that utilizes both classical and machine learning potentials. It allows for various force computations and supports learning and simulating neural network potentials. Through validation, it has been proven to be a useful toolkit for molecular simulations with machine learning potentials.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2021)

Article Chemistry, Multidisciplinary

DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science

Mufei Li et al.

Summary: Deep Graph Library (DGL)-LifeSci is an open-source toolkit for deep learning on graphs in life science, based on RDKit, PyTorch, and Deep Graph Library (DGL). It enables GNN-based modeling on custom datasets for molecular property prediction, reaction prediction, and molecule generation, with command-line interfaces for modeling without programming and deep learning background. Pretrained models and well-optimized modules are provided for modeling flexibility and improved speed compared to previous implementations.

ACS OMEGA (2021)

Article Multidisciplinary Sciences

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Alexander Rives et al.

Summary: The deep contextual language model trained through unsupervised learning on protein sequences contains information about biological properties, has a multiscale structural organization, and can be used to improve predictions for protein mutational effects, secondary structure, and long-range contacts.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)

Article Multidisciplinary Sciences

Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

Summary: Proteins are essential for life, and accurate prediction of their structures is a crucial research problem. Current experimental methods are time-consuming, highlighting the need for accurate computational approaches to address the gap in structural coverage. Despite recent progress, existing methods fall short of atomic accuracy in protein structure prediction.

NATURE (2021)

Article Computer Science, Artificial Intelligence

Prediction of chemical compounds properties using a deep learning model

Mykola Galushka et al.

Summary: This study presents a new deep learning model for conducting preliminary screening of chemical compounds in-silico and accurately predicting their properties. This approach has the potential to provide a more efficient pathway for pharmaceutical companies to discover new medications.

NEURAL COMPUTING & APPLICATIONS (2021)

Article Multidisciplinary Sciences

Geometric deep learning of RNA structure

Raphael J. L. Townshend et al.

Summary: The study introduces a machine learning approach that can accurately identify RNA structures without assumptions about their defining characteristics, trained with only a small amount of known data. This approach outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges.

SCIENCE (2021)

Article Chemistry, Multidisciplinary

GNINA 1.0: molecular docking with deep learning

Andrew T. McNutt et al.

Summary: Molecular docking software Gnina 1.0, utilizing convolutional neural networks as scoring functions, outperforms AutoDock Vina in redocking and cross-docking tasks when binding pockets are explicitly defined. The ensemble of CNNs shows good generalization to unseen proteins and ligands, producing scores that correlate well with known binding poses. The 1.0 version of GNINA is available under an open source license for use as a molecular docking tool.

JOURNAL OF CHEMINFORMATICS (2021)

Article Chemistry, Multidisciplinary

Evidential Deep Learning for Guided Molecular Property Prediction and Discovery

Ava P. Soleimany et al.

Summary: This paper introduces a new approach to uncertainty quantification for neural network-based molecular structure-property prediction using evidential deep learning, which enables calibrated predictions, sample-efficient training, and improved experimental validation rates in the chemical and physical sciences.

ACS CENTRAL SCIENCE (2021)

Article Chemistry, Physical

Benchmarking graph neural networks for materials chemistry

Victor Fung et al.

Summary: The study found that in the materials field, graph neural networks perform better and have more flexibility in input with compositionally diverse datasets compared to traditional models. However, GNNs also have some weaknesses, such as high data requirements, which need further improvement.

NPJ COMPUTATIONAL MATERIALS (2021)

Article Biochemistry & Molecular Biology

Protein Docking Model Evaluation by Graph Neural Networks

Xiao Wang et al.

Summary: Protein-protein interactions are crucial in cellular processes, with computational methods being developed to predict complex structures, such as the deep learning-based GNN-DOVE which outperformed existing methods. GNN-DOVE extracts interface areas using a graph neural network, utilizing atom properties and inter-atomic distances as features for model evaluation.

FRONTIERS IN MOLECULAR BIOSCIENCES (2021)

Article Biochemistry & Molecular Biology

Cross-docking benchmark for automated pose and ranking prediction of ligand binding

Shayne D. Wierbowski et al.

PROTEIN SCIENCE (2020)

Article Chemistry, Physical

Machine Learning for Molecular Simulation

Frank Noé et al.

Annual Review of Physical Chemistry (2020)

Article Chemistry, Medicinal

LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening

Viet-Khoa Tran-Nguyen et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Article Chemistry, Physical

Machine-guided representation for accurate graph-based molecular machine learning

Gyoung S. Na et al.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2020)

Article Chemistry, Multidisciplinary

Elucidating the multiple roles of hydration for accurate protein-ligand binding prediction via deep learning

Amr H. Mahmoud et al.

COMMUNICATIONS CHEMISTRY (2020)

Article Chemistry, Medicinal

Comparative Assessment of Scoring Functions: The CASF-2016 Update

Minyi Su et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2019)

Review Mathematical & Computational Biology

Progress in molecular docking

Jiyu Fan et al.

QUANTITATIVE BIOLOGY (2019)

Article Chemistry, Medicinal

Most Ligand-Based Classification Benchmarks Reward Memorization Rather than Generalization

Izhar Wallach et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)

Article Chemistry, Medicinal

KDEEP: Protein-Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

Jose Jimenez et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2018)

Article Chemistry, Physical

SchNet - A deep learning architecture for molecules and materials

K. T. Schuett et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Article Chemistry, Multidisciplinary

MoleculeNet: a benchmark for molecular machine learning

Zhenqin Wu et al.

CHEMICAL SCIENCE (2018)

Article Chemistry, Medicinal

Protein-Ligand Scoring with Convolutional Neural Networks

Matthew Ragoza et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2017)

Article Chemistry, Multidisciplinary

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

J. S. Smith et al.

CHEMICAL SCIENCE (2017)

Article Multidisciplinary Sciences

Quantum-chemical insights from deep tensor neural networks

Kristof T. Schuett et al.

NATURE COMMUNICATIONS (2017)

Article Chemistry, Medicinal

Boosting Docking-Based Virtual Screening with Deep Learning

Janaina Cruz Pereira et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2016)

Article Chemistry, Medicinal

Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Sereina Riniker et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2015)

Article Chemistry, Physical

ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB

James A. Maier et al.

JOURNAL OF CHEMICAL THEORY AND COMPUTATION (2015)

Article Chemistry, Medicinal

Reverse docking: a powerful tool for drug repositioning and drug rescue

Prashant S. Kharkar et al.

FUTURE MEDICINAL CHEMISTRY (2014)

Article Chemistry, Medicinal

Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

David Ryan Koes et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2013)

Article Physics, Multidisciplinary

Machine learning of molecular electronic properties in chemical compound space

Gregoire Montavon et al.

NEW JOURNAL OF PHYSICS (2013)

Article Biochemistry & Molecular Biology

BioLiP: a semi-manually curated database for biologically relevant ligand-protein interactions

Jianyi Yang et al.

NUCLEIC ACIDS RESEARCH (2013)

Review Biochemistry & Molecular Biology

Exploring Chemical Space for Drug Discovery Using the Chemical Universe Database

Jean-Louis Reymond et al.

ACS CHEMICAL NEUROSCIENCE (2012)

Article Chemistry, Medicinal

Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery

Xuan-Yu Meng et al.

Current Computer-Aided Drug Design (2012)

Article Biochemistry & Molecular Biology

Structure of recombinant ricin A chain at 2.3 Å

Debra Mlsna et al.

PROTEIN SCIENCE (2010)

Article Biochemistry & Molecular Biology

Effects of protein conformation in docking: improved pose prediction through protein pocket adaptation

Ajay N. Jain

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2009)

Article Biochemistry & Molecular Biology

Recommendations for evaluation of computational methods

Ajay N. Jain et al.

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2008)

Article Biochemistry & Molecular Biology

Bias, reporting, and sharing: computational evaluations of docking methods

Ajay N. Jain

JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN (2008)

Article Chemistry, Physical

A coarse-grained protein-protein potential derived from an all-atom force field

Nathalie Basdevant et al.

JOURNAL OF PHYSICAL CHEMISTRY B (2007)

Article Chemistry, Multidisciplinary

Development and testing of a general amber force field

JM Wang et al.

JOURNAL OF COMPUTATIONAL CHEMISTRY (2004)