The Collaborative Drug Discovery (CDD) Database

2013 ◽  
pp. 139-154 ◽  
Author(s):  
Sean Ekins ◽  
Barry A. Bunin
Keyword(s):  
Drug Discovery ◽  
Collaborative Drug Discovery

INSIDE INDUSTRY

2013 ◽  
Vol 17 (08) ◽  
pp. 58-62
Keyword(s):  
Drug Discovery ◽  
Life Sciences ◽  
Regional Office ◽  
Drug Discovery Program ◽  
Discovery Program ◽  
Collaborative Drug Discovery ◽  
Future Leaders

GE Healthcare Life Sciences and Osaka University team up to develop future leaders of Japan's life sciences sector. Quintiles acquires Novella Clinical. Tim Reiner joins Mundipharma's regional office. Daiichi Sankyo to expand its collaborative drug discovery program to Germany, Switzerland, and Austria. Eurofins Panlabs strengthens drug discovery services with GE Healthcare's Cytiva™ Cardiomyocytes.

scholarly journals FL-QSAR: a federated learning based QSAR prototype for collaborative drug discovery

2020 ◽  
Author(s):  
Shaoqi Chen ◽  
Dongyu Xue ◽  
Guohui Chuai ◽  
Qiang Yang ◽  
Qi Liu
Keyword(s):  
Drug Discovery ◽  
Quantitative Structure Activity Relationship ◽  
Privacy Preserving ◽  
Qsar Modeling ◽  
Qsar Analysis ◽  
Source Codes ◽  
Shared Information ◽  
Financial Interests ◽  
Collaborative Drug Discovery ◽  
Public Collaboration

AbstractMotivationQuantitative structure-activity relationship (QSAR) analysis is commonly used in drug discovery. Collaborations among pharmaceutical institutions can lead to a better performance in QSAR prediction, however, intellectual property and related financial interests remain substantially hindering inter-institutional collaborations in QSAR modeling for drug discovery.ResultsFor the first time, we verified the feasibility of applying the horizontal federated learning (HFL), which is a recently developed collaborative and privacy-preserving learning framework to perform QSAR analysis. A prototype platform of federated-learning-based QSAR modeling for collaborative drug discovery, i.e, FL-QSAR, is presented accordingly. We first compared the HFL framework with a classic privacy-preserving computation framework, i.e., secure multiparty computation (MPC) to indicate its difference from various perspective. Then we compared FL-QSAR with the public collaboration in terms of QSAR modeling. Our extensive experiments demonstrated that (1) collaboration by FL-QSAR outperforms a single client using only its private data, and (2) collaboration by FL-QSAR achieves almost the same performance as that of collaboration via cleartext learning algorithms using all shared information. Taking together, our results indicate that FL-QSAR under the HFL framework provides an efficient solution to break the barriers between pharmaceutical institutions in QSAR modeling, therefore promote the development of collaborative and privacy-preserving drug discovery with extendable ability to other privacy-related biomedical areas.Availability and implementationThe source codes of the federated learning simulation and FL-QSAR are available on the GitHub: https://github.com/bm2-lab/FL-QSAR

scholarly journals FL-QSAR: a federated learning-based QSAR prototype for collaborative drug discovery

2020 ◽  
Author(s):  
Shaoqi Chen ◽  
Dongyu Xue ◽  
Guohui Chuai ◽  
Qiang Yang ◽  
Qi Liu
Keyword(s):  
Drug Discovery ◽  
Quantitative Structure Activity Relationship ◽  
Privacy Preserving ◽  
Supplementary Information ◽  
Qsar Modeling ◽  
Qsar Analysis ◽  
Source Codes ◽  
Financial Interests ◽  
Collaborative Drug Discovery ◽  
Public Collaboration

Abstract Motivation Quantitative structure-activity relationship (QSAR) analysis is commonly used in drug discovery. Collaborations among pharmaceutical institutions can lead to a better performance in QSAR prediction, however, intellectual property and related financial interests remain substantially hindering inter-institutional collaborations in QSAR modeling for drug discovery. Results For the first time, we verified the feasibility of applying the horizontal federated learning (HFL), which is a recently developed collaborative and privacy-preserving learning framework to perform QSAR analysis. A prototype platform of federated-learning-based QSAR modeling for collaborative drug discovery, i.e. FL-QSAR, is presented accordingly. We first compared the HFL framework with a classic privacy-preserving computation framework, i.e. secure multiparty computation to indicate its difference from various perspective. Then we compared FL-QSAR with the public collaboration in terms of QSAR modeling. Our extensive experiments demonstrated that (i) collaboration by FL-QSAR outperforms a single client using only its private data, and (ii) collaboration by FL-QSAR achieves almost the same performance as that of collaboration via cleartext learning algorithms using all shared information. Taking together, our results indicate that FL-QSAR under the HFL framework provides an efficient solution to break the barriers between pharmaceutical institutions in QSAR modeling, therefore promote the development of collaborative and privacy-preserving drug discovery with extendable ability to other privacy-related biomedical areas. Availability and implementation The source codes of FL-QSAR are available on the GitHub: https://github.com/bm2-lab/FL-QSAR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

2017 ◽  
Vol 22 (3) ◽  
pp. 555-565 ◽  
Author(s):  
Sean Ekins ◽  
Anna Coulon Spektor ◽  
Alex M. Clark ◽  
Krishna Dole ◽  
Barry A. Bunin
Keyword(s):  
Drug Discovery ◽  
Collaborative Drug Discovery
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