Topology-Based and Conformation-Based Decoys Database: An Unbiased Online Database for Training and Benchmarking Machine-Learning Scoring Functions

Zhang, Xujun; Shen, Chao; Wang, Tianyue; Kang, Yu; Li, Dan; Pan, Peichen; Wang, Jike; Wang, Gaoang; Deng, Yafeng; Xu, Lei; Cao, Dong‐Sheng; Hou, Tingjun; Wang, Zhe

doi:10.1021/acs.jmedchem.3c00801

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…ToCoDecoy 53 has been proposed to expand virtual screening datasets while preventing additional biases by involving topology‐based and conformation‐based decoy generation. This approach was used to curate the ToCoDDB dataset, 73 which provides a more comprehensive and unbiased resource for evaluating PLI models in the screening task.…”

Section: Evaluating Generalizability Of Structure‐based Pli Modelsmentioning

confidence: 99%

Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies

Moon,

Zhung,

Kim

2024

WIREs Comput Mol Sci

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Accurate and rapid prediction of protein–ligand interactions (PLIs) is the fundamental challenge of drug discovery. Deep learning methods have been harnessed for this purpose, yet the insufficient generalizability of PLI prediction prevents their broader impact on practical applications. Here, we highlight the significance of PLI model generalizability by conceiving PLIs as a function defined on infinitely diverse protein–ligand pairs and binding poses. To delve into the generalization challenges within PLI predictions, we comprehensively explore the evaluation strategies to assess the generalizability fairly. Moreover, we categorize structure‐based PLI models with leveraged strategies for learning generalizable features from structure‐based PLI data. Finally, we conclude the review by emphasizing the need for accurate pose‐predicting methods, which is a prerequisite for more accurate PLI predictions.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Chemoinformatics Structure and Mechanism > Computational Biochemistry and Biophysics

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Section: Evaluating Generalizability Of Structure‐based Pli Modelsmentioning

confidence: 99%

Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies

Moon,

Zhung,

Kim

2024

WIREs Comput Mol Sci

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Binding Affinity Prediction with 3D Machine Learning: Training Data and Challenging External Testing

Gómez-Tamayo,

Cao,

Ahmad

et al. 2024

Preprint

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Protein-ligand binding affinity prediction is one of the major challenges in computational assisted drug discovery. An active area of research uses machine learning (ML) models trained on 3D structures of protein ligand complexes to predict binding modes, discriminate active and inactives, or predict affinity. Methodological advances in deep learning, and artificial intelligence along with increased experimental data (3D structures and bioactivities) has led to many studies using different architectures, representation, and features. Unfortunately, many models do not learn details of interactions or the underlying physics that drive protein-ligand affinity, but instead just memorize patterns in the available training data with poor generalizability and future use. In this work we incorporate “dense”, feature rich datasets that contain up to several thousand analogue molecules per drug discovery target. For the training set, PDBbind dataset is used with enrichment from 8 internal lead optimization (LO) datasets and inactive and decoy poses in a variety of combinations. A variety of different model architectures was used and the model performance was validated using the binding affinity for 12 internal LO and 6 ChEMBL external test sets. Results show a significant improvement in the performance and generalization power, especially for virtual screening and suggest promise for the future of ML protein-ligand affinity prediction with a greater emphasis on training using datasets that capture the rich details of the affinity landscape.

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Topology-Based and Conformation-Based Decoys Database: An Unbiased Online Database for Training and Benchmarking Machine-Learning Scoring Functions

Cited by 2 publications

References 25 publications

Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies

Toward generalizable structure‐based deep learning models for protein–ligand interaction prediction: Challenges and strategies

Binding Affinity Prediction with 3D Machine Learning: Training Data and Challenging External Testing

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