Spectrum of deep learning algorithms in drug discovery

Phytochemical analysis and antifungal potential of two Launaea mucronata (Forssk.) Muschl and Launaea nudicaulis (L.) Hook.fil. wildly growing in Anbar province, Iraq

ETHNOPHARMACOLOGICAL RELEVANCE

Plant fungi are a serious problem in agriculture. Even though synthetic fungicides are an efficient control method, several negative side effects emerge from their extensive use, such as health problems, environmental pollution, and the emergence of resistant microorganisms. Thus, it is becoming more and more urgent to search for alternative control methods.

AIM OF THE STUDY

The aim of our study was to analyze phytochemical composition and antifungal potential of Launaea mucronata (Forssk.) Muschl. and Launaea nudicaulis (L.) Hook. fil. wildly growing in Anbar province, Iraq. In addition, molecular analysis was used to identify the plants species and molecular docking analysis was investigated between the major phytochemicals present in these plants and three selected fungal proteins, in order to assess the antifungal activity of the selected biochemicals against these proteins.

MATERIALS AND METHODS

Molecular analysis was performed using ITS sequencing protocol. The phytochemical analysis was done using GC-MS technique, while molecular docking analysis was performed by FRED application between selected compounds from each plant and three enzymes: 17β-hydroxysteroid dehydrogenase, endochitinase, and 14-α-demethylase. Finally, the antifungal activity was assessed by measuring inhibition percentage of Fusarium solani and Macrophomina phaseolina growth treated with ethanomethanolic extract of each plant.

RESULTS

Molecular analysis identified the selected plants as L. mucronata and L. nudicaulis, with an ITS region of 600 bp. Phytochemical analysis of Launaea spp. reported the presence of 35 compounds in each ethanomethanolic extract, belonging to different classes. L. mucronata was mainly formed of lupeol (9.33%), whereas L. nudicaulis extract was dominated by the heterocyclic compound 4-(3-methoxyphenoxy)-1,2,5-oxadiazol-3-amine (20.2%). Furthermore, molecular docking analysis showed that 4H-pyran-4-one,2,3-dihydro-3,5-dihydroxy-6-methyl from L. mucronata and gulonic acid Ƴ-lactone from L. nudicaulis possessed the highest affinity score to 17-β-hydroxysteroid dehydrogenase (-4.584 and -7.811 kcal/mol, respectively). Sucrose from L. mucronata and glutaric acid, di(3,4-difluorobenzyl) ester from L. nudicaulis gave the highest affinity to endochitinase (-7.979 and - 8.446 kcal/mol, respectively). In addition, sterol 14-α-demethylase was affinitive to sucrose from L. mucronata and glutaric acid, di(3,4-difluorobenzyl) ester from L. nudicaulis via energetic score of -10.002 and -9.582 kcal/mol, respectively. Both extracts exhibited antifungal activity against F. solani and M. phaseolina in a dose-dependent manner.

CONCLUSIONS

This study confirms the antifungal potential of both Launaea spp. explained by the presence of phytochemicals with antimicrobial properties. These compounds have potential to be used as new drugs to treat infectious diseases caused by pathogens. Consequently, plants from Launaea genus could be a raw material for many studies such as therapeutic, taxonomical, drug modelling, and antifungal agent.

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database

Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in predicting pharmacological inhibitors of EGLN1 using a deep learning-based architecture, one of its subsidiaries. Egl nine homolog 1 (EGLN1) inhibition prevents poly ubiquitination-mediated proteosomal destruction HIF-1α. The pharmacological interventions aimed at stabilizing HIF-1α have the potential to be a promising treatment option for a range of human diseases, including ischemic stroke. To unveil a novel EGLN1 inhibitor from marine natural products, a custom-based virtual screening was carried out using a Deep Convolutional Neural Network (DCNN) architecture, docking, and molecular dynamics simulation. The custom DCNN model was optimized and further employed to screen marine natural products from the CMNPD database. The docking was performed as a secondary strategy for screened hits. Molecular dynamics (MD) and molecular mechanics/generalized Born surface area (MM-GBSA) were used to analyze inhibitor binding and identify key interactions. The findings support the claim that deep learning-based virtual screening is a rapid, reliable and accurate method of identifying highly contributing drug candidates (EGLN1 inhibitors). This study demonstrates that deep learning architecture can significantly accelerate drug discovery and development, and provides a solid foundation for using (Z)-2-ethylhex-2-enedioic acid [(Z)-2-ethylhex-2-enedioic acid] as a potential EGLN1 inhibitor for treating various health complications.Communicated by Ramaswamy H. Sarma.

A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques

Computational methods have gained prominence in healthcare research. The accessibility of healthcare data has greatly incited academicians and researchers to develop executions that help in prognosis of cancer drug response. Among various computational methods, machine-learning (ML) and deep-learning (DL) methods provide the most consistent and effectual approaches to handle the serious aftermaths of the deadly disease and drug administered to the patients. Hence, this systematic literature review has reviewed researches that have investigated drug discovery and prognosis of anticancer drug response using ML and DL algorithms. Fot this purpose, PRISMA guidelines have been followed to choose research papers from Google Scholar, PubMed, and Sciencedirect websites. A total count of 105 papers that align with the context of this review were chosen. Further, the review also presents accuracy of the existing ML and DL methods in the prediction of anticancer drug response. It has been found from the review that, amidst the availability of various studies, there are certain challenges associated with each method. Thus, future researchers can consider these limitations and challenges to develop a prominent anticancer drug response prediction method, and it would be greatly beneficial to the medical professionals in administering non-invasive treatment to the patients.

Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

Deep Learning Approach for Discovery of In Silico Drugs for Combating COVID-19

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than −18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19.

Enhancing Clinical Translation of Cancer Using Nanoinformatics

Application of drugs in high doses has been required due to the limitations of no specificity, short circulation half-lives, as well as low bioavailability and solubility. Higher toxicity is the result of high dosage administration of drug molecules that increase the side effects of the drugs. Recently, nanomedicine, that is the utilization of nanotechnology in healthcare with clinical applications, has made many advancements in the areas of cancer diagnosis and therapy. To overcome the challenge of patient-specificity as well as time- and dose-dependency of drug administration, artificial intelligence (AI) can be significantly beneficial for optimization of nanomedicine and combinatorial nanotherapy. AI has become a tool for researchers to manage complicated and big data, ranging from achieving complementary results to routine statistical analyses. AI enhances the prediction precision of treatment impact in cancer patients and specify estimation outcomes. Application of AI in nanotechnology leads to a new field of study, i.e., nanoinformatics. Besides, AI can be coupled with nanorobots, as an emerging technology, to develop targeted drug delivery systems. Furthermore, by the advancements in the nanomedicine field, AI-based combination therapy can facilitate the understanding of diagnosis and therapy of the cancer patients. The main objectives of this review are to discuss the current developments, possibilities, and future visions in naoinformatics, for providing more effective treatment for cancer patients.