A5080525121- Computational Drug Discovery Methods
- Folate and B Vitamins Research
- Fault Detection and Control Systems
- SARS-CoV-2 and COVID-19 Research
- Pharmacogenetics and Drug Metabolism
- Metabolomics and Mass Spectrometry Studies
- Advanced Control Systems Optimization
- Machine Learning in Materials Science
- COVID-19 Clinical Research Studies
- Analytical Chemistry and Chromatography
- Mineral Processing and Grinding
- Drug Transport and Resistance Mechanisms
- Protein Structure and Dynamics
- vaccines and immunoinformatics approaches
- Spectroscopy and Chemometric Analyses
- Dental Radiography and Imaging
- Electric Power Systems and Control
- Machine Fault Diagnosis Techniques
- Protein Degradation and Inhibitors
- Medical Imaging and Analysis
- Ubiquitin and proteasome pathways
- Hydraulic and Pneumatic Systems
- Neural dynamics and brain function
- Industrial Engineering and Technologies
- Advanced X-ray and CT Imaging
National Center for Advancing Translational Sciences
2016-2025
National Institutes of Health
2016-2025
Ruselprom (Russia)
2021-2024
Imperial College London
2020-2023
Skolkovo Institute of Science and Technology
2022
Mitsubishi Tanabe Pharma Corporation
2021
Pfizer (United States)
2016-2021
Weatherford College
2021
Russian Cancer Research Center NN Blokhin
2019-2020
Government of the United States of America
2020
The method for QSAR modelling of rat acute toxicity based on the combination QNA (Quantitative Neighbourhoods Atoms) descriptors, PASS (Prediction Activity Spectra Substances) predictions and self-consistent regression (SCR) is presented. predicted biological activity profiles are used as independent input variables with SCR. models were developed using LD50 values compounds tested rats four types administration (oral, intravenous, intraperitoneal, subcutaneous). proposed was evaluated set...
Background:Humans are exposed to thousands of man-made chemicals in the environment. Some mimic natural endocrine hormones and, thus, have potential be disruptors. Most these never been tested for their ability interact with estrogen receptor (ER). Risk assessors need tools prioritize evaluation costly vivo tests, instance, within U.S. EPA Endocrine Disruptor Screening Program.Objectives:We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity...
Background: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect EDCs causing adverse health effects in humans wildlife has led to development scientific regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) vitro computational modeling. Objectives: In support Disruptor Screening Program, U.S. Environmental...
Significance COVID-19 has caused more than 2.5 million deaths worldwide. It is imperative that we develop therapies can mitigate the effect of disease. While searching for individual drugs this purpose been met with difficulties, synergistic drug combinations offer a promising alternative. However, lack high-quality training data pertaining to makes it challenging use existing machine learning methods effective novel combination prediction tasks. Our proposed approach addresses challenge by...
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple transparent means to flag potential hazards or group compounds into categories for read-across. However, there has been growing concern that disproportionally too many chemicals toxic, which questions their reliability toxicity markers. Conversely, the rigorously developed properly validated statistical QSAR models can accurately reliably predict of chemical; however, use hampered by lack...
Antiviral drug development for coronavirus disease 2019 (COVID-19) is occurring at an unprecedented pace, yet there are still limited therapeutic options treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using silico approaches, we prioritized 73 combinations 32 potential activity SARS-CoV-2 and then tested them vitro. Sixteen synergistic eight antagonistic...
Many of the structures in PubChem are annotated with activities determined high-throughput screening (HTS) assays. Because nature these assays, activity data typically strongly imbalanced, a small number active compounds contrasting very large inactive compounds. We have used several such imbalanced HTS assays to test and develop strategies efficiently build robust QSAR models from sets. Different descriptor types [Quantitative Neighborhoods Atoms (QNA) "biological" descriptors] were...
The National Center for Advancing Translational Sciences (NCATS) has developed an online open science data portal its COVID-19 drug repurposing campaign - named OpenData with the goal of making across a range SARS-CoV-2 related assays available in real-time. cover wide spectrum life cycle, including both viral and human (host) targets. In total, over 10,000 compounds are being tested full concentration-response ranges from multiple annotated small molecule libraries, approved drug,...
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Abstract Assessment of the interactions between a drug and its protein target in physiologically relevant cellular environment constitutes major challenge pre-clinical discovery space. The Cellular Thermal Shift Assay (CETSA) enables such an assessment by quantifying changes thermal stability proteins upon ligand binding intact cells. Here, we present development validation homogeneous, standardized, target-independent, high-throughput (384- 1536-well formats) CETSA platform that uses split...
Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches expedite drug development, screen environmental chemicals, thus significantly reduce associated time costs. There is a strong need interest in the development of computational that yield reliable predictions toxicity, many approaches, including recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on collection, curation,...
Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested 12 major end points comprising over 10,000 unique molecules. then employed these data build validate binary quantitative structure–activity relationship (QSAR) models. All trained achieved correct classification rate above 0.60 positive predictive value 0.50. To...
OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying chemical safety assessment requirements REACH legislation as it supports access experimental data, (Quantitative) Structure-Activity Relationship models, toxicological information through integrating platform that adheres regulatory OECD principles. Initial research defined essential components...
Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in U.S. with more than 100 000 deaths per year. As up to 30% all ADRs believed be caused by drug–drug interactions (DDIs), typically mediated cytochrome P450s, possibilities predict DDIs from existing knowledge important. We collected data public sources on 1485, 2628, 4371, and 27 966 possible four P450 isoforms 1A2, 2C9, 2D6, 3A4 for 55, 73, 94, 237 drugs, respectively. For each these sets, we developed validated...
Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated production chemical biological data, much them freely accessible through literature aggregator services such as ChEMBL PubChem. Here, we explore how to use this comprehensive mapping biology space support large-scale quantitative structure–activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines multitask approaches together generate...
Kratom is a botanical substance that marketed and promoted in the US for pharmaceutical opioid indications despite having no Food Drug Administration approved uses. contains over forty alkaloids including two partial agonists at mu receptor, mitragynine 7-hydroxymitragynine, have been subjected to FDA's scientific medical evaluation. However, pharmacological toxicological data remaining are limited. Therefore, we applied Public Health Assessment via Structural Evaluation (PHASE) protocol...
The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against classical machine learning for a typical drug-discovery project. Inhibition the potassium ion channel, whose alpha subunit is encoded by human ether-à-go-go-related gene (hERG), leads to prolonged QT interval cardiac action potential and significant safety pharmacology target development new medicines. Several computational approaches have been employed develop prediction models assessment hERG...
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has prompted researchers to pivot their efforts finding antiviral compounds and vaccines. In this study, we focused on the human host cell transmembrane protease serine (TMPRSS2), which plays an important role in viral life cycle by cleaving spike protein initiate membrane fusion. TMPRSS2 is attractive target received attention for development of drugs against SARS Middle East syndrome. Starting with comparative...
Abstract In the existing quantitative structure–activity relationship (QSAR) methods any molecule is represented as a single point in many-dimensional space of molecular descriptors. We propose new QSAR approach based on Quantitative Neighbourhoods Atoms (QNA) descriptors, which characterize each atom and depend whole structure. 'Star Track' methodology set points two-dimensional QNA With our method estimate target property chemical compound calculated average value function descriptors...
Toxicity of chemical compound is a complex phenomenon that may be caused by its interaction with different targets in the organism. Two distinct types toxicity can broadly specified: first one strong compound's single target (e.g. AChE inhibition); while second moderate many various targets. Computer program PASS predicts about 2500 kinds biological activities based on structural formula compounds. Prediction robust analysis structure-activity relationships for 60,000 biologically active...
The evaluation of possible interactions between chemical compounds and antitarget proteins is an important task the research development process. Here, we describe validation QSAR models for prediction end-points, created on basis multilevel quantitative neighborhoods atom descriptors self-consistent regression. Data 4000 interacting with 18 (13 receptors, 2 enzymes, 3 transporters) were used to model 32 sets end-points (IC(50), K(i), K(act)). Each set was randomly divided into training test...
Abstract In the past few years, study of therapeutic RNA nanotechnology has expanded tremendously to encompass a large group interdisciplinary sciences. It is now evident that rationally designed programmable nanostructures offer unique advantages in addressing contemporary challenges such as distinguishing target cell types and ameliorating disease. However, maximize benefit these nanostructures, it essential understand immunostimulatory aptitude tools identify potential complications. This...
We describe a novel approach to RBF approximation, which combines two new elements: (1) linear radial basis functions and (2) weighting the model by each descriptor's contribution. Linear allow one achieve more accurate predictions for diverse data sets. Taking into account contribution of descriptor produces similarity values used development. The method was validated on 14 public sets comprising nine physicochemical properties five toxicity endpoints. also compared with different QSAR...