A5085528183- Computational Drug Discovery Methods
- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- Pharmacogenetics and Drug Metabolism
- Cholinesterase and Neurodegenerative Diseases
- Cell Image Analysis Techniques
- Drug-Induced Hepatotoxicity and Protection
- Machine Learning in Materials Science
- Colorectal Cancer Treatments and Studies
- Animal testing and alternatives
- Machine Learning in Bioinformatics
- Gene expression and cancer classification
- Cardiac electrophysiology and arrhythmias
- Lung Cancer Treatments and Mutations
- Metabolomics and Mass Spectrometry Studies
- Chemistry and Chemical Engineering
- Virus-based gene therapy research
- Analytical Methods in Pharmaceuticals
- Microbial Metabolic Engineering and Bioproduction
- Angiogenesis and VEGF in Cancer
- Lipid metabolism and disorders
- Cancer Genomics and Diagnostics
- Alzheimer's disease research and treatments
- Respiratory viral infections research
- Machine Learning in Healthcare
University of Cambridge
2020-2023
Optibrium (United Kingdom)
2020
Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities chemical and biological data to predict cardiotoxicity, using recently released DICTrank set from United States FDA. We found that such data, including protein targets, especially those related ion channels (e.g., hERG), physicochemical properties electrotopological state), peak concentration plasma offer strong predictive...
Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating withdrawals. Over the last decade, existing suite of
Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating withdrawals. The existing suite of vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there considerable interest enhancing silico prediction DILI because it allows for the evaluation large sets more quickly cost-effectively, particularly early stages projects. In this study, we aim study ML models...
Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, comprehensive benchmarking such has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR CARNIVAL) with networks (the smaller Omnipath...
The acid dissociation constant (pKa) has an important influence on molecular properties crucial to compound development in synthesis, formulation, and optimization of absorption, distribution, metabolism, excretion properties. We will present a method that combines quantum mechanical calculations, at semi-empirical level theory, with machine learning accurately predict pKa for diverse range mono- polyprotic compounds. resulting model been tested two external data sets, one specifically used...
Uncontrolled angiogenesis is a common denominator underlying many deadly and debilitating diseases such as myocardial infarction, chronic wounds, cancer, age-related macular degeneration. As the current range of FDA-approved angiogenesis-based medicines are far from meeting clinical demands, vast reserve natural products traditional Chinese medicine (TCM) offers an alternative source for developing pro-angiogenic or anti-angiogenic modulators. Here, we investigated 100 medicine-derived...
Modern drug discovery projects are plagued with high failure rate many of which have safety as the underlying cause. The process involves selecting right compounds from a...
Abstract Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities various chemical and biological data to predict cardiotoxicity, using recently released Drug-Induced Cardiotoxicity Rank (DICTrank) dataset from United States FDA. We analyzed diverse set sources, including physicochemical properties, annotated mechanisms action (MOA), Cell Painting, Gene Expression, more, identify...
Abstract Background Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, comprehensive benchmarking such has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR CARNIVAL) with networks (the...
Abstract Background A key histopathological hallmark of Alzheimer’s disease (AD) is the presence neurofibrillary tangles aggregated microtubule-associated protein tau in neurons. Anle138b a small molecule which has previously shown efficacy mice reducing aggregates and rescuing AD phenotypes. Methods In this work, we employed bioinformatics analysis—including pathway enrichment causal reasoning—of an vitro tauopathy model. The model consisted cultured rat cortical neurons either unseeded or...
Understanding the Mechanism of Action (MoA) a compound is an often challenging but equally crucial aspect drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim predict direct targets, or attempt understand modulated downstream pathways signalling proteins. Such require extensive coding experience results are optimised for further computational processing, making them difficult wet-lab scientists perform, interpret...
Abstract This white paper details the research conducted by Ignota Labs using their advanced causal and explainable AI technology, SAFEPATH , to analyse mechanisms of hepatotoxicity for two EGFR-TKI inhibitors, Erlotinib Gefitinib, latter having an as yet unknown mechanism toxicity. The known UGT1A1-mediated toxicity was recovered, a novel sphingolipid metabolism mechansim Gefitinib hypothesised subsequently experimentally validated. Crucially, we were also able suggest reason observed...
Abstract Background Understanding the mechanism of action (MoA) a compound is an often challenging but equally crucial aspect drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim predict direct targets, or attempt understand modulated downstream pathways signalling proteins. Such require extensive coding experience results are optimised for further computational processing, making them difficult wet-lab scientists...