应用机器学习方法构建药物分子解离速率常数的预测模型

doi:10.3866/PKU.WHXB201907006

Abstract

Abstract:

An increasing number of recent studies have shown that the binding kinetics of a drug molecule to its target correlates strongly with its efficacy in vivo. Therefore, ligand optimization oriented to improved binding kinetics provides new ideas for rational drug design. Currently, ligand binding kinetics is modeled mainly through extensive molecular dynamics simulations, which limits its application to real-world problems. The present study aimed at obtaining a general-purpose quantitative structure-kinetics relationship (QSKR) model for predicting the dissociation rate constant (k_off) of a ligand based on its complex structure. This type of model is expected to be suitable for high-throughput tasks in structure-based drug design. We collected the experimentally measured k_off values for 406 ligand molecules from literature, and then constructed a three-dimensional structural model for each protein-ligand complex through molecular modeling. A training set was compiled using 60% of those complexes while the remaining 40% were assigned to two test sets. Based on distance-dependent protein-ligand atom pair descriptors, a random forest algorithm was adopted to derive a QSKR model. Various random forest models were then generated based on the descriptor sets obtained under different conditions, such as distance cutoff, bin width, and feature selection criteria. The cross-validation results of those models were then examined. It was observed that the optimal model was obtained when the distance cutoff was 15 Å (1 Å = 0.1 nm), the bin width was 3 Å, and feature selection variance level was 2. The final QSKR model produced correlation coefficients around 0.62 on the two independent test sets. This level of accuracy is at least comparable to that of the predictive models described in literature, which are typically computationally much more expensive. Our study attempts to address the issue of predicting k_off values in drug design. We hope that it can provide inspiration for further studies by other researchers.

Key words: Dissociation rate constant, Ligand binding kinetics, Random forest model, Protein-ligand interaction, Structure-based drug design

MSC2000:

O641

Minyi Su,Huisi Liu,Haixia Lin,Renxiao Wang. Machine-Learning Model for Predicting the Rate Constant of ProteinLigand Dissociation[J].Acta Physico-Chimica Sinica, 2020, 36(1): 1907006.

Figures/Tables 10

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References 33

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Distance cutoff /Å	Bin width/Å ^a
6	1, 2, 3, 6
9	1, 3, 9
12	1, 2, 3, 4, 6, 12
15	1, 3, 5, 15

Bin width/Å	Bin width/Å	Feature selection variance level
Bin width/Å	Bin width/Å	0	1	2
6	1	0.548	0.507	0.393
	2	0.524	0.406	0.304
	3	0.509	0.484	0.546
	6	0.496	0.526	0.519
9	1	0.507	0.437	0.408
	3	0.563	0.576	0.447
	9	0.511	0.551	0.482
12	1	0.557	0.436	0.489
	2	0.562	0.416	0.568
	3	0.526	0.504	0.619
	4	0.553	0.501	0.363
	6	0.461	0.626	0.536
	12	0.425	0.356	0.604
15	1	0.317	0.604	0.487
	3	0.573	0.551	0.671
	5	0.417	0.574	0.585
	15	0.569	0.532	0.585

Data set	R		RMSE
Data set	Model 1	Reference model	Model 1	Reference model
internal validation set	0.671	0.436	1.03	1.18
external test set	0.623	0.252	1.06	1.06
HSP90 test set	0.625	0.375	0.85	0.98

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Machine-Learning Model for Predicting the Rate Constant of ProteinLigand Dissociation

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