Ligand-based approaches to activity prediction for the early stage of structure–activity–relationship progression

Maeda, Itsuki; Sato, Akinori; Tamura, Shunsuke; Miyao, Tomoyuki

doi:10.1007/s10822-022-00449-2

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…The transformed feature vectors of each atom are merged into single vector submitted to a fully-connected neural network with several hidden layer producing an output probability. Herein, a previously implemented MPNN architecture [ 35 ] originally proposed by Tang et al [ 36 ] was used. In analogy to FCNN and FCNN_sep, two distinct MPNNs were generated based on a single CGR as input (MPNN) or three separate subgraphs representing the MMP core and substituents, respectively (MPNN_sep).…”

Section: Methodsmentioning

confidence: 99%

Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity

2023

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Activity cliffs (AC) are formed by pairs of structural analogues that are active against the same target but have a large difference in potency. While much of our knowledge about ACs has originated from the analysis and comparison of compounds and activity data, several studies have reported AC predictions over the past decade. Different from typical compound classification tasks, AC predictions must be carried out at the level of compound pairs representing ACs or nonACs. Most AC predictions reported so far have focused on individual methods or comparisons of two or three approaches and only investigated a few compound activity classes (from 2 to 10). Although promising prediction accuracy has been reported in most cases, different system set-ups, AC definitions, methods, and calculation conditions were used, precluding direct comparisons of these studies. Therefore, we have carried out a large-scale AC prediction campaign across 100 activity classes comparing machine learning methods of greatly varying complexity, ranging from pair-based nearest neighbor classifiers and decision tree or kernel methods to deep neural networks. The results of our systematic predictions revealed the level of accuracy that can be expected for AC predictions across many different compound classes. In addition, prediction accuracy did not scale with methodological complexity but was significantly influenced by memorization of compounds shared by different ACs or nonACs. In many instances, limited training data were sufficient for building accurate models using different methods and there was no detectable advantage of deep learning over simpler approaches for AC prediction. On a global scale, support vector machine models performed best, by only small margins compared to others including simple nearest neighbor classifiers. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity

2023

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Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint

Syahdi,

Jasial,

Maeda

et al. 2024

ACS Omega

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Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), and their combinations, are frequently conducted in modern drug discovery campaigns. As a form of combination, an amalgamation of methods from ligand- and structure-based information, termed hybrid VS approaches, has been extensively investigated such as using interaction fingerprints (IFPs) in combination with machine learning (ML) models. This approach has the potential to prioritize active compounds in terms of protein–ligand binding and ligand structural characteristics, which is assumed to be difficult using either one of the approaches. Herein, we present an IFP, named the fragmented interaction fingerprint (FIFI), for hybrid VS approaches. FIFI is constructed from the extended connectivity fingerprint atom environments of a ligand proximal to the protein residues in the binding site. Each unique ligand substructure within each amino acid residue is encoded as a bit in FIFI while retaining sequence order. From the retrospective evaluation of activity prediction using a limited number and variety of active compounds for six biological targets, FIFI consistently showed higher prediction accuracy than that using previously proposed IFPs. For the same data sets, the screening performance of LBVS, SBVS sequential VS, parallel VS, and other hybrid VS approaches was investigated. Compared to these approaches, FIFI in combination with ML showed overall stable and high prediction accuracy, except for one target: the kappa opioid receptor, where the extended connectivity fingerprint combined with ML models showed better performance than other approaches by wide margins.

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Ligand-based approaches to activity prediction for the early stage of structure–activity–relationship progression

Cited by 2 publications

References 25 publications

Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity

Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity

Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint

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