Dataset Information

Improved method of structure-based virtual screening based on ensemble learning.

ABSTRACT: Virtual screening has become a successful alternative and complementary technique to experimental high-throughput screening technologies for drug design. Since the scoring function of docking software cannot predict binding affinity accurately, how to improve the hit rate remains a common issue in structure-based virtual screening. This paper proposed a target-specific virtual screening method based on ensemble learning named ENS-VS. In this method, protein-ligand interaction energy terms and structure vectors of the ligands were used as a combination descriptor. Support vector machine, decision tree and Fisher linear discriminant classifiers were integrated into ENS-VS for predicting the activity of the compounds. The results showed that the enrichment factor (EF) 1% of ENS-VS was 6 times higher than that of Autodock vina. Compared with the newest virtual screening method SIEVE-Score, the mean EF 1% and AUC of ENS-VS (mean EF 1% = 52.77, AUC = 0.982) were statistically significantly higher than those of SIEVE-Score (mean EF 1% = 42.64, AUC = 0.912) on DUD-E datasets; and the mean EF 1% and AUC of ENS-VS (mean EF 1% = 29.73, AUC = 0.793) were also higher than those of SIEVE-Score (mean EF 1% = 25.56, AUC = 0.765) on eight DEKOIS datasets. ENS-VS also showed significant improvements compared with other similar research. The source code is available at https://github.com/eddyblue/ENS-VS.

SUBMITTER: Li J

PROVIDER: S-EPMC9049841 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improved method of structure-based virtual screening based on ensemble learning.

Li Jin J Liu WeiChao W Song Yongping Y Xia JiYi J

RSC advances 20200219 13

Virtual screening has become a successful alternative and complementary technique to experimental high-throughput screening technologies for drug design. Since the scoring function of docking software cannot predict binding affinity accurately, how to improve the hit rate remains a common issue in structure-based virtual screening. This paper proposed a target-specific virtual screening method based on ensemble learning named ENS-VS. In this method, protein-ligand interaction energy terms and st ...[more]

PMID: 35492172

Similar Datasets

Project description:Structure-based virtual screening (SBVS) is a widely used method in silico drug discovery. It necessitates a receptor structure or binding site to predict the binding pose and fitness of a ligand. Therefore, the performance of the SBVS is affected by the protein conformation. The most frequently used method in SBVS is the protein-ligand docking program, which utilizes atomic distance-based scoring functions. Hence, they are highly prone to sensitivity towards variation in receptor structure, and it is reported that the conformational change significantly drops the performance of the docking program. To address the problem, we have introduced a novel program of SBVS, named PL-PatchSurfer. This program makes use of molecular surface patches and the Zernike descriptor. The surfaces of the pocket and ligand are segmented into several patches by the program. These patches are then mapped with physico-chemical properties such as shape and electrostatic potential before being converted into the Zernike descriptor, which is rotationally invariant. A complementarity between the protein and the ligand is assessed by comparing the descriptors and geometric distribution of the patches in the molecules. A benchmarking study showed that PL-PatchSurfer2 was able to screen active molecules regardless of the receptor structure change with fast speed. However, the program could not achieve high performance for the targets that the hydrogen bonding feature is important such as nuclear hormone receptors. In this paper, we present the newer version of PL-PatchSurfer, PL-PatchSurfer3, which incorporates two new features: a change in the definition of hydrogen bond complementarity and consideration of visibility that contains curvature information of a patch. Our evaluation demonstrates that the new program outperforms its predecessor and other SBVS methods while retaining its characteristic tolerance to receptor structure changes. Interested individuals can access the program at kiharalab.org/plps3.

Dataset Information

Improved method of structure-based virtual screening based on ensemble learning.

Publications

Improved method of structure-based virtual screening based on ensemble learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets