1,424 research outputs found

    De novo drug design through artificial intelligence: an introduction

    Get PDF
    Developing new drugs is a complex and formidable challenge, intensified by rapidly evolving global health needs. De novo drug design is a promising strategy to accelerate and refine this process. The recent introduction of Generative Artificial Intelligence (AI) algorithms has brought new attention to the field and catalyzed a paradigm shift, allowing rapid and semi-automatic design and optimization of drug-like molecules. This review explores the impact of de novo drug design, highlighting both traditional methodologies and the recently introduced generative algorithms, as well as the promising development of Active Learning (AL). It places special emphasis on their application in oncological drug development, where the need for novel therapeutic agents is urgent. The potential integration of these AI technologies with established computational and experimental methods heralds a new era in the rapid development of innovative drugs. Despite the promising developments and notable successes, these technologies are not without limitations, which require careful consideration and further advancement. This review, intended for professionals across related disciplines, provides a comprehensive introduction to AI-driven de novo drug design of small organic molecules. It aims to offer a clear understanding of the current state and future prospects of these innovative techniques in drug discovery

    MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products

    Get PDF
    The enantioselective Brønsted acid-catalyzed α-amidoalkylation reaction is a useful procedure is for the production of new drugs and natural products. In this context, Chiral Phosphoric Acid (CPA) catalysts are versatile catalysts for this type of reactions. The selection and design of new CPA catalysts for diferent enantioselective reactions has a dual interest because new CPA catalysts (tools) and chiral drugs or materials (products) can be obtained. However, this process is difcult and time consuming if approached from an experimental trial and error perspective. In this work, an Heuristic Perturbation-Theory and Machine Learning (HPTML) algorithm was used to seek a predictive model for CPA catalysts performance in terms of enantioselectivity in α-amidoalkylation reactions with R2=0.96 overall for training and validation series. It involved a Monte Carlo sampling of>100,000 pairs of query and reference reac‑ tions. In addition, the computational and experimental investigation of a new set of intermolecular α-amidoalkylation reactions using BINOL-derived N-trifylphosphoramides as CPA catalysts is reported as a case of study. The model was implemented in a web server called MATEO: InterMolecular Amidoalkylation Theoretical Enantioselectivity Optimization, available online at: https://cptmltool.rnasa-imedir.com/CPTMLTools-Web/mateo. This new user-friendly online computational tool would enable sustainable optimization of reaction conditions that could lead to the design of new CPA catalysts along with new organic synthesis products.Ministerio de Ciencia e Innovación ( PID2019104148 GB-I00; PID2022-137365NB-I00), Gobierno Vasco IT1558-2

    RECENT ADVANCES IN MOLECULAR MEDICINE AND TRANSLATIONAL RESEARCH

    Get PDF
    ABSTRACT BOO

    Computational methods in drug repurposing and natural product based drug discovery

    Get PDF
    For a few decades now, computation methods have been widely used in drug discovery or drug repurposing process, especially when saving time and money are important factors. Development of bioinformatics, chemoinformatics, molecular modelling techniques and machine or deep learning tools, as well as availability of various biological and chemical databases, have had a significant impact on improving the process of obtaining successful drug candidates. This dissertation describes the role of natural products in drug discovery, as well as presents several computational methods used in drug discovery and drug repurposing. Application of these methods is presented with the example of searching for potential drug treatment options for the COVID-19 disease. The disease is caused by the novel coronavirus SARS-CoV-2, which was first discovered in December 2019 and has caused the death of more than 5.6 million people worldwide (until January 2022). Findings from two research projects, which aimed to identify potential inhibitors of main protease of SARS-CoV-2, are presented in this work. Moreover, a summary on COVID-19 treatment possibilities has been included. In the first project, a ligand-based virtual screening of around 360,000 compounds from natural products databases, as well as approved and withdrawn drugs databases was conducted, followed by molecular docking and molecular dynamics simulations. Moreover, computational predictions of toxicity and cytochrome activity profiles for selected candidates were provided. Twelve candidates as SARS-CoV-2 main protease inhibitors were identified - among them novel drug candidates, as well as existing drugs. The second project was focused on finding potential inhibitors from plants (Reynoutria japonica and Reynoutria sachalinensis) and was based on molecular docking studies, followed by in vitro studies of the activity of selected compounds, extract, and fractions from those plants against the enzyme. Several natural compounds were identified as promising candidates for SARS-CoV-2 main protease inhibitors. Additionally, butanol fraction of Ryenoutria rhizomes extracts also showed inhibitory activity on the enzyme. Suggested drugs, natural compounds and plant extracts should be further investigated to confirm their potential as COVID-19 therapeutic options. Presented workflow could be used for investigation of compounds for other biological targets and different diseases in the future research projects.Seit einigen Jahrzehnten werden bei der Entwicklung und Repositionierung von Arzneimitteln rechenintensive computergestützte Methoden eingesetzt, insbesondere da Zeit- und Kostenersparnis wichtige Faktoren sind. Die Weiterentwicklung der Bioinformatik und Chemoinformatik und die damit einhergehende Optimierung von molekularen Modellierungstechniken und Tools für maschinelles sowie tiefes Lernen ermöglicht die Verarbeitung von großen biologischen und chemischen Datenbanken und hat einen erheblichen Einfluss auf die Verbesserung des Prozesses zur Gewinnung erfolgreicher Arzneimittelkandidaten. In dieser Dissertation wird die Rolle von Naturstoffen bei der Entwicklung von Arzneimitteln beschrieben, und es werden verschiedene computergestützte Methoden vorgestellt, die bei der Entdeckung von Arzneimitteln und der Repositionierung von Arzneimitteln eingesetzt werden. Die Anwendung dieser Methoden wird am Beispiel der Suche nach potenziellen medikamentösen Behandlungsmöglichkeiten für die Krankheit COVID-19 vorgestellt. Die Krankheit wird durch das neuartige Coronavirus SARS-CoV-2 ausgelöst, das erst im Dezember 2019 entdeckt wurde und bisher (bis Januar 2022) weltweit mehr als 5,6 Millionen Menschen das Leben gekostet hat. In dieser Arbeit werden Ergebnisse aus zwei Forschungsprojekten vorgestellt, die darauf abzielten, potenzielle Hemmstoffe der Hauptprotease von SARS-CoV-2 zu identifizieren. Außerdem wird ein Überblick über die Behandlungsmöglichkeiten von COVID-19 gegeben. Im ersten Projekt wurde ein ligandenbasiertes virtuelles Screening von rund 360.000 Kleinstrukturen aus Naturstoffdatenbanken sowie aus Datenbanken für zugelassene und zurückgezogene Arzneimittel durchgeführt, gefolgt von molekularem Docking und Molekulardynamiksimulationen. Darüber hinaus wurden für ausgewählte Kandidaten rechnerische Vorhersagen zur Toxizität und zu Cytochrom-P450-Aktivitätsprofilen erstellt. Es wurden zwölf Kandidaten als SARS-CoV-2-Hauptproteaseinhibitoren identifiziert - darunter sowohl neuartige als auch bereits vorhandene Arzneimittel. Das zweite Projekt konzentrierte sich auf die Suche nach potenziellen Inhibitoren aus Pflanzen (Reynoutria japonica und Reynoutria sachalinensis) und basierte auf molekularen Docking-Studien, gefolgt von In-vitro-Studien der Aktivität ausgewählter Verbindungen, Extrakte und Fraktionen aus diesen Pflanzen gegen das Enzym. Mehrere Naturstoffe wurden als vielversprechende Kandidaten für SARS-CoV-2- Hauptproteaseinhibitoren identifiziert. Außerdem zeigte die Butanolfraktion von Ryenoutria Rhizomextrakten ebenfalls eine hemmende Wirkung auf das Enzym. Die vorgeschlagenen Arzneimittel, Naturstoffe und Pflanzenextrakte sollten weiter untersucht werden, um ihr Potenzial als COVID-19-Therapieoptionen zu bestätigen. Der vorgestellte Arbeitsablauf könnte in zukünftigen Forschungsprojekten zur Untersuchung von Verbindungen für andere biologische Ziele und verschiedene Krankheiten verwendet werden

    Computational Approaches to Drug Profiling and Drug-Protein Interactions

    Get PDF
    Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

    Development of Novel Cellular Assay Model and Therapeutic Deep Eutectic Solvents to Optimize the Activity of Anticancer Agents

    Get PDF
    Multidrug resistance (MDR) is the major burden behind chemotherapeutic treatment failure. It is the principal mechanism by which cancer cells evade chemotherapeutic treatment. As a result, aggressive cancer cells survive and continue uncontrolled cell division. Multidrug resistance affects survival rate of almost all types of cancer patients and death toll rises at an alarming rate. There are seven different mechanisms for evolving MDR. The most common mechanism in efflux activity of overexpressed ABC transporters. MRP1 is a prominent ABC transporter that pumps out a wide variety of anticancer drugs from the cells and thereby reduces intracellular drug concentrations and develops chemoresistance. Currently there are several protocols available to assess interaction between MRP1 and probable substrate anticancer drugs. However, these protocols have several limitations in common, such as expansive instrument set up, trained personnel, complex image analysis, separation of drugs from cell lysate, preparation of membrane vesicle, and results are based on accumulation of secondary fluorescence or radiolabeled substrates. To the best of our knowledge, there are no known protocols that can directly detect interaction between MPP1 and chemotherapeutic agents and categorize as MRP1 substrates. To solve this issue, in first project we developed an easy to follow and efficient novel mammalian cell-based efflux assaying using HPLC-UV technique to detect whether MRP1 considers anticancer drug as substrate and directly pumps the drug of the cancer cells. We chose MDCK-II parental and MDCK-II MRP1 overexpressed cells to establish the assay. To evaluate the efficacy of novel protocol, the result was compared with a known MRP1 substrate anticancer drug vincristine (positive control). To conduct the assay, we chose total seven MRP1 modulator anticancer drugs identified previously in our laboratory. We exclusively focused on extracellular media for pumped-out drugs from both cell lines. Initially, incubation (1 hour) and efflux time points (2 hours) were optimized. In the final step, parental and MRP1 overexpressed cells were incubated with drugs for 1 hour. After that, cells were washed and replaced with fresh transport buffer and waited for 2 hours to collect pumped out drugs from the cells. Then the solution was injected into HPLC for determining concentration. Our research idea is that if the drug works as MRP1 substrate, it will be pumped out directly by MRP1 and extracellular concentration will significantly increase compared to parental cells (no MRP1 overexpression). Our idea worked perfectly and we identified some anticancer drugs namely, alisertib, mesalamine and celecoxib that are highly susceptible to MRP1 mediated drug resistance. Next, we validated our novel protocol using popular MTT assay. We used same cell lines for MTT. From MTT we noticed that for these substrate drugs cell viability was high in MRP1 overexpressed cells compared to parental cells. It confirmed the outcome of the novel efflux assay. The novel protocol will pioneer direct and rapid detection of new MRP1 substrates with accuracy. It could also be applied to other prominent ABC transporters to identify specific substrates. The novel assay will also promote development of ABC transporter specific inhibitors to inhibit activity of transporters and restore the pharmacological potential of chemotherapeutic agents

    Nanomaterial fate and bioavailability in freshwater environments

    Get PDF
    Given the widespread use of silver nanomaterials (AgNM), their accidental or intentional release into the environment is inevitable. AgNM release into riverine systems is a daily occurrence, and following their release, they will undoubtedly interact with naturally occurring organic and inorganic particulates and sediment interfaces. At this point, AgNM's long-term threat to freshwater ecosystems is unclear. We must develop our understanding of AgNM fate, toxicity, and bioavailability using testing approaches that systematically investigate AgNM environmental interaction within single-factor and multifactor systems. This body of research aimed to comprehensively examine selected AgNM particles that were tracked within parallel fate scenarios and toxicity and bioavailability studies. Results showed contrasting behavior between the two tested AgNM. Findings also demonstrated that low shear flow is a significant factor influencing the flocculation and settling rates of AgNM, which differentially regulated the persistence and residence time of aqueous phase AgNM within simulated riverine systems. Experiments with low shear flow showed a significant increase in AgNM water column removal and modulated the physicochemistry differentially compared to quiescent systems. The findings on the influence of bed sediment interactions with waterborne AgNM demonstrated that they are a vital process that increases the transfer and exchange of AgNM from the water column to the bed. Toxicity studies showed how abiotic factors could modulate toxicity differentially between aquatic species and how inorganic and organic matter can increase and decrease AgNM toxicity. Exposure studies contrasting singular and multifactor exposures with and without low shear flow demonstrated that they modulate the exposure of AgNM significantly differently. In conclusion, the proof-of-concept flume designs for testing the environmental fate and exposure of AgNM showed promise and that, with further refinement, could be further incorporated into the life-cycle testing framework of ENMs, to produce accurate semi-empirical coefficients for environmental models for the assessment of hazard

    Química dinámica combinatoria : optimización de la química reversible y aplicación en el descubrimiento de fármacos

    Get PDF
    Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Químicas, leída el 26-01-2023Dynamic combinatorial chemistry is defined as “the chemistry under thermodynamic control”. It is based on the combination of building blocks that react with each other through reversible chemical bonds to form the final products, reaching the thermodynamic equilibrium (dynamic combinatorial library, DCL). This chemistry is able to respond to external stimuli such as pH, temperature, or the addition of a biomolecule acting as a template. For instance, adding a template will shift the equilibrium towards the formation of the compounds with higher affinity to the template.Templated-DCLs developed under physiological conditions require an effective design of the dynamic chemical system composed of a biomolecule as a template, reversible chemistry that works effectively under physiological conditions, structurally diverse building blocks compatible with the target, and an analysis method. Protein-directed dynamic combinatorial chemistry (P-D DCC) is currently a powerful and efficient tool for discovering ligands with high affinity to a protein target. In this thesis, adding two different protein targets, NCS1 and glucose oxidase, shifted the DCL equilibrium to forming the best ligands in a pool of compounds...La química dinámica combinatoria se define como la química bajo control termodinámico. Se basa en la combinación de monómeros (en inglés, building blocks), que reaccionan entre sí a través de enlaces químicos reversibles formando compuestos, hasta alcanzar el equilibrio termodinámico (librería dinámica combinatoria, DCL, del inglés dynamic combinatorial library). Esta química reversible, en unas condiciones concretas, tiene la capacidad de responder a estímulos externos como el pH, la temperatura o la adición de una biomolécula que actúe como plantilla. En este último caso, el equilibrio se desplazará hacia la formación de complejos más estables y afines por la plantilla. En condiciones fisiológicas y en presencia de una plantilla, las DCLs requieren de un sistema químico-dinámico eficiente compuesto, además de la biomolécula que actúa como plantilla, de una química reversible adecuada y de unos monómeros estructuralmente distintos compatibles con la biomolécula y del método de análisis. La química dinámica combinatoria dirigida por proteínas (en inglés, protein-directed DCC, P-D DCC) se considera actualmente una herramienta eficaz y potente para encontrar ligandos que poseen una afinidad alta por la proteína que actúa como plantilla. En esta tesis, la adición de dos proteínas diferentes como dianas, NCS1 y glucosa oxidasa, desplaza el equilibrio de la dcl hacia la formación de los ligandos más prometedores del conjunto de compuestos formados en el equilibrio..Fac. de Ciencias QuímicasTRUEunpu
    corecore