Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

Niazi, Sarfaraz K.; Mariam, Zamara

doi:10.3390/ijms241411488

Cited by 36 publications

(8 citation statements)

References 106 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We utilized four different MLTs, namely SVM, RF, ANN, and kNN, to develop highly effective predictive models. These MLTs have been employed by various researchers in a multitude of studies [ 40 ]. For example, Mpropred for the prediction of SARS-CoV-2 main protease antagonists [ 41 ], TargIDe for predicting the molecules with antibiofilm activity against Pseudomonas aeruginosa [ 42 ], EBOLApred for predicting cell entry inhibitors against the Ebola virus [ 43 ], and StackHCV for the identification of inhibitors against the NS5 protein of the Hepatitis C virus [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Gautam,

Thakur,

Rajput

et al. 2023

Viruses

View full text Add to dashboard Cite

Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the “Anti-Dengue” algorithm that predicts dengue virus inhibitors using a quantitative structure–activity relationship (QSAR) and MLTs. Using the “DrugRepV” database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model’s reliability and robustness were assessed using William’s plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. “Anti-Dengue” may be beneficial in accelerating antiviral drug development against the dengue virus.

show abstract

Section: Discussionmentioning

confidence: 99%

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Gautam,

Thakur,

Rajput

et al. 2023

Viruses

View full text Add to dashboard Cite

show abstract

“…Using regression techniques, these models establish a statistically significant relationship between chemical structures and their biological properties. Subsequent developments in machine learning have significantly expanded the capabilities of QSAR to include virtual screening, model construction, assessment of chemical risk, and evaluation of druglikelihood properties on vast data sets containing a variety of chemical structures …”

Section: Rational Drug Design Technologiesmentioning

confidence: 99%

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Nandi,

Bhaduri,

Das

et al. 2024

Mol. Pharmaceutics

View full text Add to dashboard Cite

Understanding protein sequence and structure is essential for understanding protein−protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure−activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein−protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

show abstract

“…In this section, we briefly describe these applications to provide context for the present work. There is also an emerging field of cheminformatics [33][34][35], which we leave outside of the scope of the present discussion.…”

Section: Background On Molecular Descriptorsmentioning

confidence: 99%

Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors

Mao,

Krems

2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors. Our starting point is based on three-dimensional, universal, physical descriptors derived from the properties of the distributions of the eigenvalues of Coulomb matrices. To account for the shape and composition of molecules, we combine these descriptors with six-dimensional features informed by the Gershgorin circle theorem. We use the nine-dimensional descriptors thus obtained for Gaussian process regression based on kernels with variable functional form, leading to extremely eﬃcient, low-dimensional interpolation models. The resulting models trained with 100 molecules are able to predict the product of entropy and temperature (S × T ) and zero point vibrational energy (ZPVE) with the absolute error under 1 kcal/mol for > 78 % and under 1.3 kcal/mol for > 92 % of molecules in the test data. The test data comprises 20,000 molecules with complexity varying from three atoms to 29 atoms and the ranges of S × T and ZPVE covering 36 kcal/mol and 161 kcal/mol, respectively. We also illustrate that the descriptors based on the Gershgorin circle theorem yield more accurate models of molecular entropy than those based on graph neural networks that explicitly account for the atomic connectivity of molecules.

show abstract

Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review

Cited by 36 publications

References 106 publications

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors

Contact Info

Product

Resources

About