Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small molecule drugs. AI technologies, such as generative chemistry, machine learning, and multi-property optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.Comment: 30 pages, 4 figures, 184 reference

Prediction of Ligand Activity at Subcellular Location

Understanding subcellular distribution and the mechanism of xenobiotics can help in modulating subcellular dysfunction mediated diseases. Therefore, with improved knowledge of how xenobiotics are distributed across subcellular locations and the mechanism for a specific molecule can play a crucial role in assessing drug efficacy and toxicity. Such knowledge would widen therapeutic windows by allowing specific receptors to be targeted efficiently. Based on datasets that provide information on the subcellular locations of proteins and their ligands, we developed machine learning models for 42 subcellular locations. Such models were trained and validated based on the grid search method and best models based on Cohen’s Kappa scores were selected. With the help of the state-of-the-art supercomputing facilities provided by the Texas Advanced Computing Center(TACC), we were able to develop a suite of more than 22300+ machine learning models. These machine learning models were built using 19 different fingerprints-based features for 42 different subcellular locations using 28 different ML classifiers. The web-application is available on an open portal and can be accessed at https://drugdiscovery.utep.edu/subcell/ by anyone in order to perform high-throughput cheminformatics simulations. All the data and models generated from the project are made available as open-source

A systems chemical biology study of malate synthase and isocitrate lyase inhibition in Mycobacterium tuberculosis during active and NRP growth

The ability of Mycobacterium tuberculosis (Mtb) to survive in low oxygen environments enables the bacterium to persist in a latent state within host tissues. In vitro studies of Mtb growth have identified changes in isocitrate lyase (ICL) and malate synthase (MS) that enable bacterial persistent under low oxygen and other environmentally limiting conditions. Systems chemical biology (SCB) enables us to evaluate the effects of small molecule inhibitors not only on the reaction catalyzed by malate synthase and isocitrate lyase, but the effect on the complete tricarboxylic acid cycle (TCA) by taking into account complex network relationships within that system

NETWORK INFERENCE DRIVEN DRUG DISCOVERY

The application of rational drug design principles in the era of network-pharmacology requires the investigation of drug-target and target-target interactions in order to design new drugs. The presented research was aimed at developing novel computational methods that enable the efficient analysis of complex biomedical data and to promote the hypothesis generation in the context of translational research. The three chapters of the Dissertation relate to various segments of drug discovery and development process. The first chapter introduces the integrated predictive drug discovery platform „SmartGraph”. The novel collaborative-filtering based algorithm „Target Based Recommender (TBR)” was developed in the framework of this project and was validated on a set of 28,270 experimentally determined bioactivity data points involving 1,882 compounds and 869 targets. The TBR is integrated into the SmartGraph platform. The graphical interface of SmartGraph enables data analysis and hypothesis generation even for investigators without substantial bioinformatics knowledge. The platform can be utilized in the context of target identification, drug-target prediction and drug repurposing. The second chapter of the Dissertation introduces an information theory inspired dynamic network model and the novel “Luminosity Diffusion (LD)” algorithm. The model can be utilized to prioritize protein targets for drug discovery purposes on the basis of available information and the importance of the targets. The importance of targets is accounted for in the information flow simulation process and is derived merely from network topology. The LD algorithm was validated on 8,010 relations of 794 proteins extracted from the Target Central Resource Database developed in the framework of the “Illuminating the Druggable Genome” project. The last chapter discusses a fundamental problem pertaining to the generation of similarity network of molecules and their clustering. The network generation process relies on the selection of a similarity threshold. The presented work introduces a network topology based systematic solution for selecting this threshold so that the likelihood of a reasonable clustering can be increased. Furthermore, the work proposes a solution for generating so-called “pseudo-reference clustering” for large molecular data sets for performance evaluation purposes. The results of this chapter are applicable in the lead identification and development processes

Defining the microbial effluxome in the content of the host-microbiome interaction

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ChemProt-2.0: visual navigation in a disease chemical biology database

ChemProt-2.

TIN-X:target importance and novelty explorer

Abstract Motivation The increasing amount of peer-reviewed manuscripts requires the development of specific mining tools to facilitate the visual exploration of evidence linking diseases and proteins. Results We developed TIN-X, the Target Importance and Novelty eXplorer, to visualize the association between proteins and diseases, based on text mining data processed from scientific literature. In the current implementation, TIN-X supports exploration of data for G-protein coupled receptors, kinases, ion channels, and nuclear receptors. TIN-X supports browsing and navigating across proteins and diseases based on ontology classes, and displays a scatter plot with two proposed new bibliometric statistics: Importance and Novelty. Availability and Implementation http://www.newdrugtargets.org </jats:sec

2020-06-22/23 DAILY UNM GLOBAL HEALTH COVID-19 BRIEFING

Executive Summary: NM Highlights: Balloon Fiesta postponed. Robot to sanitize ABQ International Sunport. NMSU plan for fall 2020. NM public schools reopening plan. Fewer hospitalizations for COVID-19. NM case update. US Highlights: Visa restrictions. International Highlights: Second wave in South Korea. Epidemiology: Lockdown can suppress COVID-19. Case fatality rate associated with incidence. Predictors of ICU care and ventilators. Anxiety and depression from COVID-19. Asymptomatic patients as source of infection. Heterogeneous populations affect herd immunity. Healthcare Policy Recommendations: New FDA guidance on clinical trials conduct. Practice Guidelines: The guidelines are provided on COVID-19 diagnostics (Infectious Diseases Society of America), respiratory support for COVID-19 patients and optimizing mental care delivery during COVID-19 pandemic. Drugs, Vaccines, Therapies, Clinical Trials: Antithrombotic therapy systematic review. Drug repurposing. 58 new trials. Other Science: Safety of antihypertensives (ACEs and ARBs). Low testosterone linked to escalation of care. Neurological findings and hypercoagulability