Machine Learning for Molecular Modelling in Drug Design

Ballester, Pedro J.

doi:10.3390/biom9060216

Cited by 31 publications

(18 citation statements)

References 13 publications

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“…In addition to virtual screening, de novo drug design methods [8], which generate synthesizable small molecules with high binding affinity, provide another type of computer-aided drug design direction. Artificial intelligence, e.g., machine learning and deep learning, is playing more and more important roles in the aforementioned computational methods and thus drug development [9][10][11]. In this review, we will focus on developments of the last four computational methods as well as their applications in drug screening and design.…”

Section: Introductionmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

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Section: Introductionmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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“…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]. Moreover, ML-based approaches represent a valuable resource also in drug repurposing field [59,60].…”

Section: Drug Discovery and Developmentmentioning

confidence: 99%

Artificial Intelligence in Translational Medicine

Brogi¹,

Calderone²

2021

Preprint

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The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as Machine Learning (ML)/Deep Learning (DL) and overall Artificial Intelligence (AI). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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“…The classification of RF starts at the root node, in which the data set at each node is split according to the value of descriptors that are selected such that the descriptors of different activities are predominantly moved to different branches. [74,75] Finally, the classification is obtained by averaging the results of all trees by a majority vote from each tree. [76,77] The RF classifier was generated using the randomForest package in the R programming language.…”

Section: Multivariate Analysismentioning

confidence: 99%

HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors

et al. 2020

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Hepatitis C virus (HCV) is one of the major causes of liver disease affecting an estimated 170 million people culminating in 300,000 deaths from cirrhosis or liver cancer. NS5B is one of three potential therapeutic targets against HCV (i.e., the other two being NS3/4A and NS5A) that is central to viral replication. In this study, we developed a classification structure–activity relationship (CSAR) model for identifying substructures giving rise to anti‐HCV activities among a set of 578 non‐redundant compounds. NS5B inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 independent data splits using the random forest algorithm. The modelability (MODI index) of the data set was determined to be robust with a value of 0.88 exceeding established threshold of 0.65. The predictive performance was deduced by the accuracy, sensitivity, specificity, and Matthews correlation coefficient, which was found to be statistically robust (i.e., the former three parameters afforded values in excess of 0.8 while the latter statistical parameter provided a value >0.7). An in‐depth analysis of the top 20 important descriptors revealed that aromatic ring and alkyl side chains are important for NS5B inhibition. Finally, the predictive model is deployed as a publicly accessible HCVpred web server (available at http://codes.bio/hcvpred/) that would allow users to predict the biological activity as being active or inactive against HCV NS5B. Thus, the knowledge and web server presented herein can be used in the design of more potent and specific drugs against the HCV NS5B.

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Machine Learning for Molecular Modelling in Drug Design

Abstract: Machine learning (ML) has become a crucial component of early drug discovery [...]

Cited by 31 publications

References 13 publications

A Review on Applications of Computational Methods in Drug Screening and Design

A Review on Applications of Computational Methods in Drug Screening and Design

Artificial Intelligence in Translational Medicine

HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors

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