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Overview

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Neoplasms

Hemic and Lymphatic Diseases

Small molecule drug

Disease domain score

A glimpse into the focused therapeutic areas

Technology Platform

Most used technologies in drug development

Targets

Most frequently developed targets

Disease Domain	Count

Neoplasms	4
Hemic and Lymphatic Diseases	1

Top 5 Drug Type	Count

Small molecule drug	4

Top 5 Target	Count

PKMYT1(protein kinase, membrane associated tyrosine/threonine 1)	1
VRK1(VRK serine/threonine kinase 1)	1
CCNE1(cyclin E1)	1

AbstractProtein kinase membrane-associated tyrosine/threonine 1 (PKMYT1) is a negative regulator of CDK1 activity and has been identified as a putative synthetic lethal target in cancers which upregulate cyclin E (CCNE1). We used high-throughput virtual screening to identify inhibitors of PKMYT1 with high lipophilic ligand efficiency (LLE), which were rapidly optimized to potent leads with exceptional selectivity over a key off target, WEE1. Machine learning-driven optimization rapidly reduced mouse hepatocyte clearance to acceptable levels, delivering a compound with best-in-class potency, selectivity, and pharmacokinetic profiles in relevant animal models. Tool compounds from this series were used to validate a differentiated biomarker for sensitivity to PKMYT1 inhibition in cellular models, which could significantly expand the potential patient population. In vivo proof-of-concept studies are ongoing in a variety of cell line- and patient-derived xenograft models which represent the novel biomarker previously identified, to further validate our therapeutic strategy.Citation Format: Alfie Brennan, Jan Lanz, Daniel S. Miller, Oliver Vipond, Noah Harrison, James Aaronson, Anna Hercot. Discovery, biomarker validation, and pre-clinical profiling of a novel PKMYT1 inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1813.

22 Mar 2024·Cancer Research

Abstract 5813: AI-driven identification and validation of novel synthetic lethal gene pairs through deep mining of cancer dependency data

Author: Miller, Daniel S. ; Brennan, Alfie ; Hercot, Anna ; Vipond, Oliver

AbstractThe targeting of synthetic lethal (SL) gene pairs is emerging as a new paradigm in precision oncology, with the potential to deliver treatments with higher efficacy and fewer side-effects. However, existing approaches have focused on a few well-validated pairs, and there is a clear need to expand the toolbox of potential SL targets. We have performed deep mining of cancer dependency data using a suite of machine learning tools and AI algorithms underpinned by robust statistical analysis to identify the next generation of SL gene pairs.To ensure robust, high confidence validation of multiple SL pairs simultaneously, we have used three cell lines and orthogonal assay formats. This has led to the confirmation of new druggable SL genes suitable for clinical development. We have identified the cancer subtypes with the strongest evidence of SL vulnerabilities and quantified patient populations who would most benefit from novel therapeutic agents. For one gene pair, we have generated novel best-in-class inhibitors of the target of interest, with exceptional selectivity over the SL partner, and used these chemical tools to validate the SL relationship in 2D and 3D models.Citation Format: Daniel S. Miller, Oliver Vipond, Alfie Brennan, Anna Hercot. AI-driven identification and validation of novel synthetic lethal gene pairs through deep mining of cancer dependency data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5813.

01 Nov 2019·BioinformaticsQ3 · BIOLOGY

A decision-theoretic approach to the evaluation of machine learning algorithms in computational drug discovery

Q3 · BIOLOGY

Article

Author: Cortes-Ciriano, Isidro ; Watson, Oliver P ; Taylor, Aimee R ; Watson, James A

AbstractMotivationArtificial intelligence, trained via machine learning (e.g. neural nets, random forests) or computational statistical algorithms (e.g. support vector machines, ridge regression), holds much promise for the improvement of small-molecule drug discovery. However, small-molecule structure-activity data are high dimensional with low signal-to-noise ratios and proper validation of predictive methods is difficult. It is poorly understood which, if any, of the currently available machine learning algorithms will best predict new candidate drugs.ResultsThe quantile-activity bootstrap is proposed as a new model validation framework using quantile splits on the activity distribution function to construct training and testing sets. In addition, we propose two novel rank-based loss functions which penalize only the out-of-sample predicted ranks of high-activity molecules. The combination of these methods was used to assess the performance of neural nets, random forests, support vector machines (regression) and ridge regression applied to 25 diverse high-quality structure-activity datasets publicly available on ChEMBL. Model validation based on random partitioning of available data favours models that overfit and ‘memorize’ the training set, namely random forests and deep neural nets. Partitioning based on quantiles of the activity distribution correctly penalizes extrapolation of models onto structurally different molecules outside of the training data. Simpler, traditional statistical methods such as ridge regression can outperform state-of-the-art machine learning methods in this setting. In addition, our new rank-based loss functions give considerably different results from mean squared error highlighting the necessity to define model optimality with respect to the decision task at hand.Availability and implementationAll software and data are available as Jupyter notebooks found at https://github.com/owatson/QuantileBootstrap.Supplementary informationSupplementary data are available at Bioinformatics online.

100 Deals associated with Evariste Ltd.

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100 Translational Medicine associated with Evariste Ltd.

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Pipeline

Pipeline Snapshot as of 23 Nov 2024

The statistics for drugs in the Pipeline is the current organization and its subsidiaries are counted as organizations,Early Phase 1 is incorporated into Phase 1, Phase 1/2 is incorporated into phase 2, and phase 2/3 is incorporated into phase 3

Preclinical

4

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Current Projects

Drug(Targets)	Indications	Global Highest Phase

CCNE1 Addicted Tumors(Evariste) ( CCNE1 )	Neoplasms More	Preclinical
Haem Malignancies(Evariste)	Hematologic Neoplasms More	Preclinical
VRK1 inhibitors(Evariste) ( VRK1 )	Neoplasms More	Preclinical
PKMYT1 inhibitor(Evariste) ( PKMYT1 )	Neoplasms More	Preclinical