Artificial intelligence, machine learning, and drug repurposing in cancer

Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means. Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication. Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.Peer reviewe

Applications of Artificial Intelligence in Healthcare

Now in these days, artificial intelligence (AI) is playing a major role in healthcare. It has many applications in diagnosis, robotic surgeries, and research, powered by the growing availability of healthcare facts and brisk improvement of analytical techniques. AI is launched in such a way that it has similar knowledge as a human but is more efficient. A robot has the same expertise as a surgeon; even if it takes a longer time for surgery, its sutures, precision, and uniformity are far better than the surgeon, leading to fewer chances of failure. To make all these things possible, AI needs some sets of algorithms. In Artificial Intelligence, there are two key categories: machine learning (ML) and natural language processing (NPL), both of which are necessary to achieve practically any aim in healthcare. The goal of this study is to keep track of current advancements in science, understand technological availability, recognize the enormous power of AI in healthcare, and encourage scientists to use AI in their related fields of research. Discoveries and advancements will continue to push the AI frontier and expand the scope of its applications, with rapid developments expected in the future

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality

Antibody structure prediction using deep learning

The aim of this doctoral project was to improve the prediction of antibody structures and optimise the use of existing antibody data. As a starting point, we developed ABlooper, a tool that predicts the position of backbone atoms for CDR loops in antibodies. In Chapter 2, we present the methods and results for ABlooper. This tool has enabled the modelling of antibody structures at previously unprecedented speeds, predicting the CDR loops for one hundred structures in less than five seconds. However, ABlooper has some limitations: it cannot model side-chain atoms and sometimes generates unphysical structures, which need to be corrected using computationally expensive methods. To overcome these limitations, we developed ImmuneBuilder, a novel method based on the recently released AlphaFold2 models. ImmuneBuilder is a set of deep learning-based tools for modelling antibodies, nanobodies, and TCRs. In contrast to ABlooper, this method can accurately predict the position of both backbone and side-chain atoms. Moreover, it predicts the structure of the entire variable domain, not just that of the CDRs. Chapter 3 describes the ImmuneBuilder method in detail. With next-generation sequencing studies routinely generating millions of antibody sequences, effectively searching this data has become a challenge. In Chapter 4, we present KA-Search a tool that allows for the rapid search of billions of antibody sequences by sequence identity across either the whole chain, the complementarity-determining regions, or a user defined antibody region. We show KA-Search in operation on the 2.4 billion antibody sequences available in the OAS database. In addition to next-generation sequencing data, there is a vast amount of literature that focuses on a small number of antibodies. For these smaller scale experiments, the amount of available information for each individual antibody is much greater. To facilitate the retrieval of this information, we developed the Patent and Literature Antibody Database (PLAbDab), an evolving reference set of functionally diverse, literature-annotated antibody sequences and structures. Chapter 5 describes how this database was generated and gives examples of how it could be used. The final chapter draws conclusions and proposes future avenues of research

The Use of Computational Methods in the Toxicological Assessment of Chemicals in Food: Current Status and Future Prospects

A wide range of chemicals are intentionally added to, or unintentially found in, food products, often in very small amounts. Depending on the situation, the experimental data needed to complete a dietary risk assessment, which is the scientific basis for protecting human health, may not be available or obtainable, for reasons of cost, time and animal welfare. For example, toxicity data are often lacking for the metabolites and degradation products of pesticide active ingredients. There is therefore an interest in the development and application of efficient and effective non-animal methods for assessing chemical toxicity, including Quantitative Structure-Activity Relationship (QSAR) models and related computational methods. This report gives an overview of how computational methods are currently used in the field of food safety by national regulatory bodies, international advisory organisations and the food industry. On the basis of an international survey, a comprehensive literature review and a detailed QSAR analysis, a range of recommendations are made with the long-term aim of promoting the judicious use of suitable QSAR methods. The current status of QSAR methods is reviewed not only for toxicological endpoints relevant to dietary risk assessment, but also for Absorption, Distribution, Metabolism and Excretion (ADME) properties, which are often important in discriminating between the toxicological profiles of parent compounds and their reaction products. By referring to the concept of the Threshold of Toxicological Concern (TTC), the risk assessment context in which QSAR methods can be expected to be used is also discussed. This Joint Research Centre (JRC) Reference Report provides a summary and update of the findings obtained in a study carried out by the JRC under the terms of a contract awarded by the European Food Safety Authority (EFSA).JRC.DG.I.6-Systems toxicolog

ARTIFICIAL INTELLIGENCE IN TACKLING CORONAVIRUS AND FUTURE PANDEMICS

SARS-COV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) was initially tested in Wuhan City, China, in December 2019 and had a devastating impact worldwide, exterminating more than 6 million people as of September 2022. It became the biggest worldwide health crisis since the 1918 influenza outbreak. Viruses generally mutate randomly, so predicting how SARS-CoV-2 will transform over the next few months or years and which forms will predominate is impossible. The possibilities for virus mutation, in theory, are practically endless. Enabling researchers to determine which antibodies have the potential to be most effective against existing and future variations could help machine learning to assist in drug discovery. In the COVID-19 pandemic, AI has benefited four key areas: diagnosis, clinical decision-making for public health, virtual assistance, and therapeutic research. This study conducted a discourse analysis and textual evaluation of AI (deep learning and machine learning) concerning the COVID-19 outbreak. Further, this study also discusses the latest inventions that can be very helpful in future pandemic detection. COVID-19 has already changed our lives, and in the future, we might be able to deal with pandemics like this with the help of AI. This review has also emphasized the legal implications of AI in the battle against COVID-19

Looking at COVID-19 from a Systems Biology Perspective

The sudden outbreak and worldwide spread of the SARS-CoV-2 pandemic pushed the scientific community to find fast solutions to cope with the health emergency. COVID-19 complexity, in terms of clinical outcomes, severity, and response to therapy suggested the use of multifactorial strategies, characteristic of the network medicine, to approach the study of the pathobiology. Proteomics and interactomics especially allow to generate datasets that, reduced and represented in the forms of networks, can be analyzed with the tools of systems biology to unveil specific pathways central to virus\u2013human host interaction. Moreover, artificial intelligence tools can be implemented for the identification of druggable targets and drug repurposing. In this review article, we provide an overview of the results obtained so far, from a systems biology perspective, in the understanding of COVID-19 pathobiology and virus\u2013host interactions, and in the development of disease classifiers and tools for drug repurposing

A review on machine learning approaches and trends in drug discovery

Abstract: Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.Instituto de Salud Carlos III; PI17/01826Instituto de Salud Carlos III; PI17/01561Xunta de Galicia; Ref. ED431D 2017/16Xunta de Galicia; Ref. ED431D 2017/23Xunta de Galicia; Ref. ED431C 2018/4