A review on machine learning approaches and trends in drug discovery

Carracedo-Reboredo, Paula; Liñares-Blanco, Jose; Rodríguez-Fernández, Nereida; Cedrón, Francisco; Nóvoa, Francisco J.; Carballal, Adrián; Maojo, Víctor; Pazos, Alejandro; Fernández-Lozano, Carlos

doi:10.1016/j.csbj.2021.08.011

Cited by 233 publications

(151 citation statements)

References 137 publications

Supporting

Mentioning

120

Contrasting

Order By: Relevance

“…These are molecular surface area or polar surface area, molecular volume, etc. Carracedo-Reboredo et al define 4D descriptors, providing information about the interactions between ligands and protein-binding sites [ 32 ]. Apart from single numbers, descriptors could be composed of several numbers.…”

Section: Drug Design—historical Notesmentioning

confidence: 99%

Drug Design—Past, Present, Future

Doytchinova

2022

Molecules

View full text Add to dashboard Cite

Drug design is a complex pharmaceutical science with a long history. Many achievements have been made in the field of drug design since the end of 19th century, when Emil Fisher suggested that the drug–receptor interaction resembles the key and lock interplay. Gradually, drug design has been transformed into a coherent and well-organized science with a solid theoretical background and practical applications. Now, drug design is the most advanced approach for drug discovery. It utilizes the innovations in science and technology and includes them in its wide-ranging arsenal of methods and tools in order to achieve the main goal: discovery of effective, specific, non-toxic, safe and well-tolerated drugs. Drug design is one of the most intensively developing modern sciences and its progress is accelerated by the implication of artificial intelligence. The present review aims to capture some of the most important milestones in the development of drug design, to outline some of the most used current methods and to sketch the future perspective according to the author’s point of view. Without pretending to cover fully the wide range of drug design topics, the review introduces the reader to the content of Molecules’ Special Issue “Drug Design—Science and Practice”.

show abstract

Section: Drug Design—historical Notesmentioning

confidence: 99%

Drug Design—Past, Present, Future

Doytchinova

2022

Molecules

View full text Add to dashboard Cite

show abstract

“…This latter study combined standard molecular descriptors to properties derived from structure-based analyses (e.g., interaction descriptors extracted from molecular docking), allowing for a more complete and multi-factorial view. Definition of general rules able to predict both the permeability and the activity of antimicrobial compounds can greatly benefit from the application of machine learning approaches able to speed up the drug discovery process 24 , 25 . The application of these methods requires collection of data for the learning phase 26 , 27 , that highlights the need for curated molecular databases providing ready-to-use features.…”

Section: Background and Summarymentioning

confidence: 99%

AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

et al. 2022

View full text Add to dashboard Cite

Antibiotic resistance is a major threat to public health. The development of chemo-informatic tools to guide medicinal chemistry campaigns in the efficint design of antibacterial libraries is urgently needed. We present AB-DB, an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds. We considered more than 300 molecules belonging to 25 families that include the most relevant antibiotic classes in clinical use, such as β-lactams and (fluoro)quinolones, as well as inhibitors of key bacterial proteins. We provide traditional descriptors together with properties obtained with Density Functional Theory calculations. Noteworthy, AB-DB contains less conventional descriptors extracted from μs-long molecular dynamics simulations in explicit solvent. In addition, for each compound we make available force-field parameters for the major micro-species at physiological pH. With the rise of multi-drug-resistant pathogens and the consequent need for novel antibiotics, inhibitors, and drug re-purposing strategies, curated databases containing reliable and not straightforward properties facilitate the integration of data mining and statistics into the discovery of new antimicrobials.

show abstract

“…In this study, the classification models were built using the support vector machine (SVM), logistic regression (LR), k-nearest neighbor (KNN), artificial neural network (ANN), naïve Bayes (NB), random forest (RF) (with a tree number of 20 and the maximum tree depth of 15), and decision tree (DT). An in-depth description of the application of these methods in drug discovery can be obtained from some excellent studies and research papers [33,34]. All of these calculations were integrated with Orange Canvas 3.11 software (freely available at https://orange.biolab.si/, accessed on 8 March 2018).…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Guan

Fan

et al. 2022

Molecules

View full text Add to dashboard Cite

Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors.

show abstract

A review on machine learning approaches and trends in drug discovery

Abstract: Graphical abstract

Cited by 233 publications

References 137 publications

Drug Design—Past, Present, Future

Drug Design—Past, Present, Future

AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Contact Info

Product

Resources

About