scholarly journals Applications of Machine Learning in Drug Discovery I: Target Discovery and Small Molecule Drug Design

2020 ◽  
Author(s):  
John W. Cassidy
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Drug Design ◽  
Small Molecule ◽  
Target Discovery ◽  
Small Molecule Drug ◽  
Applications Of Machine Learning

Targeted Protein Degradation: "The Gold Rush is On!"

2020 ◽  
Vol 7 (1) ◽  
pp. 4-16
Author(s):  
Daria Kotlarek ◽  
Agata Pawlik ◽  
Maria Sagan ◽  
Marta Sowała ◽  
Alina Zawiślak-Architek ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Degradation ◽  
Small Molecule ◽  
Gold Rush ◽  
European Company ◽  
Drug Candidates ◽  
Protein Inhibition ◽  
Small Molecule Drug ◽  
Therapeutic Benefits ◽  
Talent Pool

Targeted Protein Degradation (TPD) is an emerging new modality of drug discovery that offers unprecedented therapeutic benefits over traditional protein inhibition. Most importantly, TPD unlocks the untapped pool of the proteome that to date has been considered undruggable. Captor Therapeutics (Captor) is the fourth global, and first European, company that develops small molecule drug candidates based on the principles of targeted protein degradation. Captor is located in Basel, Switzerland and Wroclaw, Poland and exploits the best opportunities of the two sites – experience and non-dilutive European grants, and talent pool, respectively. Through over $38 M of funding, Captor has been active in three areas of TPD: molecular glues, bi-specific degraders and direct degraders, ObteronsTM.

scholarly journals Glossary and tutorial of xenobiotic metabolism terms used during small molecule drug discovery and development (IUPAC Technical Report)

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Paul Erhardt ◽  
Kenneth Bachmann ◽  
Donald Birkett ◽  
Michael Boberg ◽  
Nicholas Bodor ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Chemical Structure ◽  
Early Stage ◽  
Xenobiotic Metabolism ◽  
Drug Discovery And Development ◽  
Small Molecule Drug ◽  
Technical Report ◽  
Wide Range ◽  
Draft Version

Abstract This project originated more than 15 years ago with the intent to produce a glossary of drug metabolism terms having definitions especially applicable for use by practicing medicinal chemists. A first-draft version underwent extensive beta-testing that, fortuitously, engaged international audiences in a wide range of disciplines involved in drug discovery and development. It became clear that the inclusion of information to enhance discussions among this mix of participants would be even more valuable. The present version retains a chemical structure theme while expanding tutorial comments that aim to bridge the various perspectives that may arise during interdisciplinary communications about a given term. This glossary is intended to be educational for early stage researchers, as well as useful for investigators at various levels who participate on today’s highly multidisciplinary, collaborative small molecule drug discovery teams.

Small-Molecule Drug Discovery in Triple Negative Breast Cancer: Current Situation and Future Directions

2021 ◽  
Vol 64 (5) ◽  
pp. 2382-2418 ◽  
Author(s):  
Minru Liao ◽  
Jin Zhang ◽  
Guan Wang ◽  
Leiming Wang ◽  
Jie Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Discovery ◽  
Small Molecule ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Current Situation ◽  
Future Directions ◽  
Small Molecule Drug

scholarly journals Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

2018 ◽  
Author(s):  
Khader Shameer ◽  
Kipp W. Johnson ◽  
Benjamin S. Glicksberg ◽  
Rachel Hodos ◽  
Ben Readhead ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Small Molecule ◽  
Chemical Space ◽  
Drug Repositioning ◽  
Chemical Properties ◽  
Support Vector ◽  
Feature Engineering ◽  
Connectivity Map ◽  
Molecular Features

ABSTRACTDrug repositioning, i.e. identifying new uses for existing drugs and research compounds, is a cost-effective drug discovery strategy that is continuing to grow in popularity. Prioritizing and identifying drugs capable of being repositioned may improve the productivity and success rate of the drug discovery cycle, especially if the drug has already proven to be safe in humans. In previous work, we have shown that drugs that have been successfully repositioned have different chemical properties than those that have not. Hence, there is an opportunity to use machine learning to prioritize drug-like molecules as candidates for future repositioning studies. We have developed a feature engineering and machine learning that leverages data from publicly available drug discovery resources: RepurposeDB and DrugBank. ChemVec is the chemoinformatics-based feature engineering strategy designed to compile molecular features representing the chemical space of all drug molecules in the study. ChemVec was trained through a variety of supervised classification algorithms (Naïve Bayes, Random Forest, Support Vector Machines and an ensemble model combining the three algorithms). Models were created using various combinations of datasets as Connectivity Map based model, DrugBank Approved compounds based model, and DrugBank full set of compounds; of which RandomForest trained using Connectivity Map based data performed the best (AUC=0.674). Briefly, our study represents a novel approach to evaluate a small molecule for drug repositioning opportunity and may further improve discovery of pleiotropic drugs, or those to treat multiple indications.

scholarly journals Harnessing the Versatility of Optical Biosensors for Target-Based Small-Molecule Drug Discovery

ACS Sensors ◽  
2016 ◽  
Vol 2 (1) ◽  
pp. 10-15 ◽  
Author(s):  
Tim Kaminski ◽  
Anders Gunnarsson ◽  
Stefan Geschwindner
Keyword(s):  
Drug Discovery ◽  
Small Molecule ◽  
Optical Biosensors ◽  
Small Molecule Drug
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