Classification of carcinogenic and mutagenic properties using machine learning method

Recent progress in machine learning approaches for predicting carcinogenicity in drug development

INTRODUCTION

This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance.

AREAS COVERED

The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency.

EXPERT OPINION

Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.

Review of machine learning and deep learning models for toxicity prediction

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

Classification analysis of fatty acid synthase inhibitors using multialgorithms on topological descriptors and structural fingerprints

Fatty acid synthase (FASN) is one of the enzymes required for fatty acid biosynthesis and is expressed as low or absent in most normal cells/tissues. However, this enzyme is upregulated in various cancer cells; hence, it can act as an important target to design and develop novel FASN inhibitors for cancer therapy. In the present investigation, a series of structurally diverse compounds that possessed FASN inhibitory activities were subjected to classification analysis using different algorithms such as support vector machine, decision tree, Naïve Bayes and random forest. The physicochemical descriptors and MACCS fingerprints were calculated using PaDEL software, and the WEKA software was utilized for the classification model building. The statistical parameters/confusion matrix calculated from the analysis revealed that the selected models have significant predictive performances. The results showed that the topological properties of the molecules are the main determinant for the activity classification. The key descriptors comprised of hydrogen bonding groups, especially acceptor (nHBAcc, minHBint9, minHBint5 and nwHBa), charge on the topological surface of the molecules (JGI10 & GGI2), ionization potential (GATS5i and GATS1i) and branching and distance between the groups (ETA_Eta_B_RC) are significantly contributed in the classification models. Further, the presence of heteroatoms (MACCSFP82, MACCSFP93 and MACCSFP131), especially nitrogen atom(s) and hydrogen bond acceptor groups (N-N group, NC(=O)N, N-C(=O)), actively contributed to the inhibitory activities. The results concluded that the topological polar properties concentrated in a specific region have significant FASN inhibitory activity. Hence, these results shall be used to develop novel molecules with increased FASN inhibitory activity.

Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space

Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that protects cells. These peptides share common structural and physicochemical properties and show different pharmaceutical applications, among which drug delivery is the most important. Due to their ability to cross the membranes by pulling high-molecular-weight polar molecules, they are termed Trojan horses. In this study, we proposed a machine learning (ML)-based framework named BChemRF-CPPred (beyond chemical rules-based framework for CPP prediction) that uses an artificial neural network, a support vector machine, and a Gaussian process classifier to differentiate CPPs from non-CPPs, using structure- and sequence-based descriptors extracted from PDB and FASTA formats. The performance of our algorithm was evaluated by tenfold cross-validation and compared with those of previously reported prediction tools using an independent dataset. The BChemRF-CPPred satisfactorily identified CPP-like structures using natural and synthetic modified peptide libraries and also obtained better performance than those of previously reported ML-based algorithms, reaching the independent test accuracy of 90.66% (AUC = 0.9365) for PDB, and an accuracy of 86.5% (AUC = 0.9216) for FASTA input. Moreover, our analyses of the CPP chemical space demonstrated that these peptides break some molecular rules related to the prediction of permeability of therapeutic molecules in cell membranes. This is the first comprehensive analysis to predict synthetic and natural CPP structures and to evaluate their chemical space using an ML-based framework. Our algorithm is freely available for academic use at http://comptools.linc.ufpa.br/BChemRF-CPPred .