Machine Learning Approaches for Improving Prediction Performance of Structure-Activity Relationship Models

In silico bioactivity prediction studies are designed to complement in vivo and in vitro efforts to assess the activity and properties of small molecules. In silico methods such as Quantitative Structure-Activity/Property Relationship (QSAR) are used to correlate the structure of a molecule to its biological property in drug design and toxicological studies. In this body of work, I started with two in-depth reviews into the application of machine learning based approaches and feature reduction methods to QSAR, and then investigated solutions to three common challenges faced in machine learning based QSAR studies. First, to improve the prediction accuracy of learning from imbalanced data, Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms combined with bagging as an ensemble strategy was evaluated. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that this method significantly outperformed other conventional methods. SMOTEENN with bagging became less effective when IR exceeded a certain threshold (e.g., \u3e40). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p \u3c 0.001, ANOVA) by 22-27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Lastly, current features used for QSAR based machine learning are often very sparse and limited by the logic and mathematical processes used to compute them. Transformer embedding features (TEF) were developed as new continuous vector descriptors/features using the latent space embedding from a multi-head self-attention. The significance of TEF as new descriptors was evaluated by applying them to tasks such as predictive modeling, clustering, and similarity search. An accuracy of 84% on the Ames mutagenicity test indicates that these new features has a correlation to biological activity. Overall, the findings in this study can be applied to improve the performance of machine learning based Quantitative Structure-Activity/Property Relationship (QSAR) efforts for enhanced drug discovery and toxicology assessments

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

Nextcast : A software suite to analyse and model toxicogenomics data

The recent advancements in toxicogenomics have led to the availability of large omics data sets, representing the starting point for studying the exposure mechanism of action and identifying candidate biomarkers for toxicity prediction. The current lack of standard methods in data generation and analysis hampers the full exploitation of toxicogenomics-based evidence in regulatory risk assessment. Moreover, the pipelines for the preprocessing and downstream analyses of toxicogenomic data sets can be quite challenging to implement. During the years, we have developed a number of software packages to address specific questions related to multiple steps of toxicogenomics data analysis and modelling. In this review we present the Nextcast software collection and discuss how its individual tools can be combined into efficient pipelines to answer specific biological questions. Nextcast components are of great support to the scientific community for analysing and interpreting large data sets for the toxicity evaluation of compounds in an unbiased, straightforward, and reliable manner. The Nextcast software suite is available at: ( https://github.com/fhaive/nextcast).(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer reviewe

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics

Transcriptomics in Toxicogenomics, Part III : Data Modelling for Risk Assessment

Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.Peer reviewe

Crow search algorithm with time varying flight length strategies for feature selection

Feature Selection (FS) is an efficient technique use to get rid of irrelevant, redundant and noisy attributes in high dimensional datasets while increasing the efficacy of machine learning classification. The CSA is a modest and efficient metaheuristic algorithm which has been used to overcome several FS issues. The flight length (fl) parameter in CSA governs crows\u27 search ability. In CSA, fl is set to a fixed value. As a result, the CSA is plagued by the problem of being hoodwinked in local minimum. This article suggests a remedy to this issue by bringing five new concepts of time dependent fl in CSA for feature selection methods including linearly decreasing flight length, sigmoid decreasing flight length, chaotic decreasing flight length, simulated annealing decreasing flight length, and logarithm decreasing flight length. The proposed approaches\u27 performance is assessed using 13 standard UCI datasets. The simulation result portrays that the suggested feature selection approaches overtake the original CSA, with the chaotic-CSA approach beating the original CSA and the other four proposed approaches for the FS task

A QSAR classification model of skin sensitization potential based on improving binary crow search algorithm

Classifying of skin sensitization using the quantitative structure-activityrelationship (QSAR) model is important. Applying descriptor selection isessential to improve the performance of the classification task. Recently, abinary crow search algorithm (BCSA) was proposed, which has been successfully applied to solve variable selection. In this work, a new time-varyingtransfer function is proposed to improve the exploration and exploitation capability of the BCSA in selecting the most relevant descriptors in QSAR classification model with high classification accuracy and short computing time.The results demonstrated that the proposed method is reliable and can reasonably separate the compounds according to sensitizers or non-sensitizerswith high classification accuracy