Using Transcriptomics Data to Understand Drug Mode-of-Action
Lead Research Organisation:
UNIVERSITY OF CAMBRIDGE
Department Name: Chemistry
Abstract
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Organisations
- UNIVERSITY OF CAMBRIDGE (Lead Research Organisation)
- UNIVERSITY OF OXFORD (Collaboration)
- Addenbrooke's Hospital (Collaboration)
- United States Environmental Protection Agency (Collaboration)
- CARDIFF UNIVERSITY (Collaboration)
- UNIVERSITY OF CAMBRIDGE (Collaboration)
- Eli Lilly and Company Limited (Student Project Partner)
People |
ORCID iD |
| Andreas Bender (Primary Supervisor) | |
| Layla Hosseini-Gerami (Student) |
Publications
Hosseini-Gerami L
(2023)
Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.
in BMC bioinformatics
Hosseini-Gerami L
(2023)
Mechanism of action deconvolution of the small-molecule pathological tau aggregation inhibitor Anle138b
in Alzheimer's Research & Therapy
Hosseini-Gerami L
(2023)
MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny.
in BMC bioinformatics
Trapotsi M
(2022)
Computational analyses of mechanism of action (MoA): data, methods and integration
in RSC Chemical Biology
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| BB/M011194/1 | 30/09/2015 | 31/03/2024 | |||
| 2110926 | Studentship | BB/M011194/1 | 30/09/2018 | 29/09/2022 | Layla Hosseini-Gerami |
| Description | Elucidating molecular mechanisms of cell stress using gene expression data and causal reasoning |
| Organisation | United States Environmental Protection Agency |
| Country | United States |
| Sector | Public |
| PI Contribution | Computational analysis (causal reasoning and pathway analysis) of compounds known to induce cell stress. |
| Collaborator Contribution | Interpretation of results and domain-specific expertise. |
| Impact | No outcomes yet. Purely computational study. |
| Start Year | 2020 |
| Description | Towards developing a potent and selective ACE2 PET radioligand for COVID-19 research |
| Organisation | Addenbrooke's Hospital |
| Country | United Kingdom |
| Sector | Hospitals |
| PI Contribution | Myself and my colleague carried out computational studies to rank candidate radioligands by potency against the ACE2 receptor, including molecular docking and FEP calculations. |
| Collaborator Contribution | Our collaborators took our prioritised candidates and are currently synthesizing them, with a view to later carry out binding and other biological assays to determine their suitability as PET radiotracers. |
| Impact | No publications yet. Disciplines involved are: computational chemistry, machine learning, molecular modelling, synthetic chemistry, radiochemistry. |
| Start Year | 2020 |
| Description | Towards developing a potent and selective ACE2 PET radioligand for COVID-19 research |
| Organisation | Cardiff University |
| Country | United Kingdom |
| PI Contribution | Myself and my colleague carried out computational studies to rank candidate radioligands by potency against the ACE2 receptor, including molecular docking and FEP calculations. |
| Collaborator Contribution | Our collaborators took our prioritised candidates and are currently synthesizing them, with a view to later carry out binding and other biological assays to determine their suitability as PET radiotracers. |
| Impact | No publications yet. Disciplines involved are: computational chemistry, machine learning, molecular modelling, synthetic chemistry, radiochemistry. |
| Start Year | 2020 |
| Description | Towards developing a potent and selective ACE2 PET radioligand for COVID-19 research |
| Organisation | University of Oxford |
| Department | Department of Chemistry |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Myself and my colleague carried out computational studies to rank candidate radioligands by potency against the ACE2 receptor, including molecular docking and FEP calculations. |
| Collaborator Contribution | Our collaborators took our prioritised candidates and are currently synthesizing them, with a view to later carry out binding and other biological assays to determine their suitability as PET radiotracers. |
| Impact | No publications yet. Disciplines involved are: computational chemistry, machine learning, molecular modelling, synthetic chemistry, radiochemistry. |
| Start Year | 2020 |
| Description | Understanding mechanisms of compound-induced toxicity using multi-modal biological data |
| Organisation | University of Cambridge |
| Department | Department of Chemistry |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Machine learning to predict toxicity endpoints using compound-perturbed gene expression data. |
| Collaborator Contribution | Factor analysis to analyse multi-modal biological data, machine learning to predict toxicity endpoints using cellular morphology data and chemical fingerprints. |
| Impact | No outcome yet. Purely computational study. |
| Start Year | 2021 |
| Title | PIDGINv4 |
| Description | Compound-structure based target prediction to elucidate drug mechanism of action. V4 is an improved version of V3, incorporating more accurate models, new functionality and more training data. |
| Type Of Technology | New/Improved Technique/Technology |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | The software is used by research groups around the world, including active drug discovery projects |
| URL | https://pidginv4.readthedocs.io/en/latest/ |