Science Pool

This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we discuss the use of machine learning approaches in large omics studies

The chapter includes:

• an overview of the typical analysis of an omics dataset using machine learning
• demonstration of how to build a model predicting drug-induced liver injury (DILI) using transcriptomics data
• best practices and pitfalls to consider during initial data exploration and model training through to validation and analysis of the final model

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This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we discuss the use of artificial intelligence, machine learning, and deep learning in computational drug discovery.

The chapter includes:

• an overview of the current state-of-the-art artificial intelligence methods which are applied in drug discovery
• a focus on structure-based and ligand-based virtual screening, library design and high throughput analysis
• further discussion on drug repurposing, de novo design, chemical reactions and synthetic accessibility, ADMET and quantum mechanics
• real life drug design case studies

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This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we discuss artificial intelligence and deep learning, and its application in computational chemistry.

The chapter includes:

• a comparison of deep learning and other machine learning approaches
• the advantages of deep learning including the facile incorporation of multitask learning and the enhancement of generative modelling
• the impact of deep learning on computational chemistry

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This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we provide the latest insights into fighting COVID-19 with technologies such as artificial intelligence.

It includes:

• an overview of the ongoing demand for efficacious drugs to treat potential vaccine-resistant COVID-19 variants in the future
• how identifying and repurposing marketed drugs for the treatment of COVID-19 plays an important role in this process 
• how artificial intelligence can be employed to speed up the drug discovery process by facilitating the selection of potential drug candidates as well as monitoring the pandemic and enabling faster diagnosis in patients
• a focus on the impact and challenges associated with artificial intelligence for drug repurposing of therapies for the treatment of COVD-19

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This article forms a book chapter in Artificial Intelligence in Drug Design.

In this review article, we provide the latest insights into de novo design approaches based on  artificial intelligence (AI) algorithms with a specific focus on ligand-based methods.

It includes:

• a background to de novo design approaches developed prior to the use of AI
• an overview of AI and commonly employed neural network architectures used in ligand based de novo design
• a list of more than 100 deep generative models reported in the literature from 2017-2020
• current applications of deep generative approaches in the drug discovery context
• an insight into future applications of deep generative models in de novo drug design 

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Transient Receptor Potential Melastatin 5 (TRPM5) is an intracellular calcium-activated cation-selective ion channel which is expressed in a variety of cells and tissues. Dysfunction of the TRPM5 channel has been linked to a number of pathological conditions including diabetes, inflammatory responses, enteric infections and parasitic infections. Identifying sufficiently selective agonists or positive modulators of the TRPM5 channel has, to date, proved challenging which has limited its potential as a drug target.   

In this publication, we focus on:

• the development of a high throughput screen using a fluorescent membrane potential assay for primary hit identification screening and selectivity assessment of TRPM5 channel positive modulators 
• use of medium and high throughput electrophysiology assays (QPatch HTX and SyncroPatch 384PE) as follow-up screens to confirm activity and selectivity of identified hits
• the SyncroPatch 384PE assay for structure-activity relationship (SAR) expansion of the identified chemical series

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Loss of heart myocardium is considered to be an irreversible process which can eventually lead to heart failure. Adult cardiomyocytes divide at a rate of less than 1% per year and no cardiac stem or progenitor cell type contribute significantly to the replacement of lost myocytes. One approach being pursued to replace lost heart muscle and regenerate the heart is the use of stem cell-derived cardiomyocytes.

In this publication, we focus on an in-depth review of the use of human pluripotent stem-cell derived cardiomyocytes in heart regeneration including:

• a background to cardiac regeneration and the approaches used to address this
• preclinical research and achievements in the use of cell therapy and stem cell-derived cardiomyocytes for the replacement of lost heart muscle 
• an overview of existing open questions such as how the technology works, the duration of effect, patient selection, immunological issues and how to reduce risk
• a summary of the clinical trials currently ongoing in this field

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P2X3 receptors play an important role in the sensitisation of nerve fibres and pain pathways. Involvement in pathways triggering cough and contribution to the pathophysiology of endometriosis and overactive bladder have also been reported. Development of P2X antagonists have been hampered by off‑target effects which include severe taste disturbances associated with blocking the P2X2/3 receptor heterotrimer.  

In this publication, we focus on:

• how eliapixant (BAY 1817080), a P2X3 receptor antagonist, is both highly potent and selective for P2X3 over other P2X subtypes in vitro including P2X2/3
• how eliapixant reduces inflammatory pain in relevant animal models
• experimental evidence that P2X3 antagonism reduces neurogenic inflammation and vaginal pain, and demonstration of the potential use of eliapixant in endometriosis

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The key to de-risking and expediting the development and approval of new antimicrobials lies in the detailed understanding of the PK/PD relationship. Understanding this relationship informs the development of optimal human dosing, maximising efficacy while minimising the potential to antimicrobial resistance.

This white paper describes and appraises today's most versatile in vitro system for the determination of in vitro PK/PD relationships between antimicrobial compounds and bacteria, fungi and viruses - the Hollow Fibre Infection Model (HFIM)

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Mass spectrometry (MS)-based ubiquitinomics allows a system-level understanding of ubiquitin signalling. 

In this publication, we focus on:

• a background to ubiquitinome profiling
• presentation of a scalable and robust workflow for deep and precise in vivo ubiquitinome profiling using DIA-MS (data-independent acquisition mass spectrometry) and neural network based data processing
• comprehensive mapping of substrates of deubiquitinase USP7, an anticancer drug target known to regulate tumour suppressor p53
• application of the method including rapid mode of action profiling of candidate drugs targeting deubiquitinases or ubiquitin ligases 

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