ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

Zhang, Xujun; Shen, Chao; Guo, Xueying; Wang, Zhe; Weng, Gaoqi; Ye, Qing; Wang, Gaoang; He, Qiaojun; Yang, Bo; Cao, Dong‐Sheng; Hou, Tingjun

doi:10.1186/s13321-021-00486-3

Cited by 9 publications

(5 citation statements)

References 16 publications

(18 reference statements)

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“…To sum up, a total of 72 MIEC-ML models were built with the above terms’ combination (Figure ). All the codes of the models and the optimal parameters can be found in the Supporting Information and also reference …”

Section: Methodsmentioning

confidence: 99%

“…All the codes of the models and the optimal parameters can be found in the Supporting Information and also reference. 86 To further test the performance of the models, data set II was employed as the additional external test set. Because the original structure of a co-crystallized NR protein can only adapt in one conformation (active state targeted by agonist or inactive state targeted by antagonist), we mapped the co-ligand into the other state of the NR protein by superimposing the corresponding active and inactive states of the protein.…”

Section: ■ Introductionmentioning

confidence: 99%

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Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

Wang

Tian

et al. 2022

J. Chem. Inf. Model.

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The mechanism of transcriptional activation/repression of the nuclear receptors (NRs) involves two main conformations of the NR protein, namely, the active (agonistic) and inactive (antagonistic) conformations. Binding of agonists or antagonists to the ligand-binding pocket (LBP) of NRs can regulate the downstream signaling pathways with different physiological effects. However, it is still hard to determine the molecular type of a LBP-bound ligand because both the agonists and antagonists bind to the same position of the protein. Therefore, it is necessary to develop precise and efficient methods to facilitate the discrimination of agonists and antagonists targeting the LBP of NRs. Here, combining structural and energetic analyses with machine-learning (ML) algorithms, we constructed a series of structure-based ML models to determine the molecular category of the LBP-bound ligands. We show that the proposed models work robustly and with high accuracy (ACC > 0.9) for determining the category of molecules derived from docking-based and crystallized poses. Furthermore, the models are also capable of determining the molecular category of ligands with dual opposite functions on different NRs (i.e., working as an agonist in one NR target, whereas functioning as an antagonist in another) with reasonable accuracy. The proposed method is expected to facilitate the determination of the molecular properties of ligands targeting the LBP of NRs with structural interpretation.

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Section: Methodsmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

Wang

Tian

et al. 2022

J. Chem. Inf. Model.

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“…At last, we sum up some freely available codes and platforms for feature generation of protein–ligand complexes and list them in Table 3, including BINANA, 83 ODDT, 136 RF‐Score, 18 DDB, 30 ASFP, 137 and so on. Here, the Artificial Intelligence based Scoring Function Platform (ASFP) is recently released by our team.…”

Section: Tools For Featurizationmentioning

confidence: 99%

Featurization strategies for protein–ligand interactions and their applications in scoring function development

Xiong

Shen

Yang

et al. 2021

WIREs Comput Mol Sci

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The predictive performance of classical scoring functions (SFs) seems to have reached a plateau. Currently, SFs relying on sophisticated machine learning techniques have shown great potential in binding affinity prediction and virtual screening. As one of the most indispensable components in the workflow of training a machine learning scoring function (MLSF), the featurization or representation process enables us to catch certain physical processes that are important for protein–ligand interactions and to obtain machine‐readable descriptors. Currently, according to how they are derived, the descriptors used in MLSFs for both continuous and binary binding affinity estimates can be grouped into two broad categories: handcrafted features and automated‐extraction features. Moreover, the automated‐extraction features emerge as a new featurization trend along with the application of deep learning algorithms. Here, we make a thorough summary of the advances in the featurization strategies for protein–ligand interactions in the context of MLSFs, with emphasis on the recently rising automated‐extraction features. We also discuss the similarity between protein–ligand interaction representations and small‐molecule representations, and the challenges confronted by the scientific community in characterizing protein–ligand interactions. We expect that this review could inspire the development of novel featurization approaches and boosted MLSFs. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Interactions

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“…Despite the usefulness of ODDT, it seems that its' development is stalled. ML-based protein-ligand interaction capturer (ML-PLIC) is a web platform [50], an enhanced version of Artificial Intelligence based Scoring Function Platform (ASFP) [51], to automatically design new ML-based scoring function. ML-PLIC provides feature vectors from PLEC, SPLIF [52], and NNScore scoring functions.…”

Section: -Introductionmentioning

confidence: 99%

REINDEER: A Protein-Ligand Feature Generator Software for Machine Learning Algorithms

Rayka

2024

Preprint

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Machine learning-based scoring functions, which apply feature-generation methods for protein-ligand representation, have become ubiquitous in the past few years for binding affinity prediction. However, most of these feature-generation techniques are hidden inside papers and their corresponding codes. In this manuscript, we introduced REINDEER software to make these methods accessible to other users. REINDEER has been developed based on minimum dependencies and parallelization aims by Python programming language. The current version of REINDEER (v0.1.0) only includes feature generation methods from RF-Score, ET-Score, ECIF∷LD-GBT, and OnionNet-2 scoring functions. REINDEER provides a command line interface, graphical user interface, and usage within Python code capabilities to access these methods. Also, a case study on PDBbind refined set v2020 is presented to evaluate REINDEER abilities. REINDEER software is available at https://github.com/miladrayka/reindeer_software.

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ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

Cited by 9 publications

References 16 publications

Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

Determination of Molecule Category of Ligands Targeting the Ligand-Binding Pocket of Nuclear Receptors with Structural Elucidation and Machine Learning

Featurization strategies for protein–ligand interactions and their applications in scoring function development

REINDEER: A Protein-Ligand Feature Generator Software for Machine Learning Algorithms

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