An Analysis of QSAR Research Based on Machine Learning Concepts

Abstract

: Quantitative Structure–Activity Relationship (QSAR) is a popular approach developed to correlate chemical molecules with their biological activities based on their chemical structures. Machine learning techniques have proved to be promising solutions to QSAR modeling. Due to the significant role of machine learning strategies in QSAR modeling, this area of research has attracted much attention from researchers. A considerable amount of literature has been published on machine learning based QSAR modeling methodologies whilst this domain still suffers from lack of a recent and comprehensive analysis of these algorithms. This study systematically reviews the application of machine learning algorithms in QSAR, aiming to provide an analytical framework. For this purpose, we present a framework called ‘ML-QSAR‘. This framework has been designed for future research to: a)facilitate the selection of proper strategies among existing algorithms according to the application area requirements, b) help to develop and ameliorate current methods and c) providing a platform to study existing methodologies comparatively. In ML-QSAR, first a structured categorization is depicted which studied the QSAR modeling research based on machine models. Then several criteria are introduced in order to assess the models. Finally, inspired by aforementioned criteria the qualitative analysis is carried out.

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“…Machine learning and artificial intelligence have been increasingly applied in various domain such as computer vision 51,52 , natural language processing [53][54][55] , drug discovery 56,57 , QSAR [58][59][60] , and genomics [61][62][63] . AI methods such as convolutional neural networks (CNNs) 64 and recurrent neural networks (RNNs) 65 that are extensively used in computer vision and natural language processing have been investigated for identifying protein binding sites in DNA and RNA sequences, and achieved state-of-the-art performance [66][67][68] .…”

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“…The increasing availability of data and modern machine learning techniques will further facilitate the development and increase the predictivity of QSARs (e.g. Pawar et al, 2019 ; Muratov et al, 2020 ; Keyvanpour and Shirzad, 2021 ; Shah et al, 2021 ). The in silico part of assessment generally starts with the QSAR analysis, e.g.…”

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“…Beyond the classical computational approaches in drug discovery, such as ligand-(mainly QSAR methods and pharmacophore modelling) [28][29][30][31] and structure-based strategies (mainly based on molecular docking and molecular dynamics) [32][33][34][35] or a combination of them [36][37][38][39], currently these computational methods are integrated with ML technologies for improving the reliability of the calculation, avoiding false positive outcomes and enhancing the success ratio in identifying safer hit compounds. Some examples are represented by QSAR-ML models [40][41][42][43], multi-and combi-QSAR approaches [44][45][46][47][48][49][50]. Furthermore, in drug discovery field, advanced computational models, based on ML technology, hve demonstrated strong potential in selecting effective hit compounds [51][52][53][54][55][56][57][58].…”