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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Updated: May 20, 2026

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

An Open-Source Framework for Mass Calculation of Antibody-Based Therapeutic Molecules

Published on: June 16, 2023

Developing a framework for understanding and enabling open source drug discovery.

Minna Allarakhia¹

¹University of Waterloo, Department of Management Sciences, 200 University Avenue W. Waterloo, ON N2L 3G1, Canada. minna@alum.mit.edu

Expert Opinion on Drug Discovery

|July 26, 2012

Summary

Open source drug discovery addresses complex challenges by leveraging collaborative models. This study proposes a framework to guide the selection of open source strategies and governance for successful knowledge sharing and product development.

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Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

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Published on: December 11, 2016

Area of Science:

Pharmaceutical Sciences
Biotechnology
Innovation Management

Background:

Open source drug discovery (OSDD) is gaining traction to navigate intricate product development.
Existing literature highlights the need for OSDD but lacks a cohesive framework for its implementation.
Complexities in knowledge, technology, and commercialization necessitate structured approaches.

Purpose of the Study:

To develop a framework for understanding how drivers influence OSDD models and governance.
To guide the selection of appropriate open source strategies and management approaches.
To facilitate successful knowledge access and product development in OSDD initiatives.

Main Methods:

Literature review on OSDD drivers and complexities.
Development of an initial framework based on participant type, strategy objective, and knowledge structure.
Analysis of factors influencing motivation, structural models, and knowledge management.

Main Results:

A framework is proposed that links participant motivation, OSDD structural models, and knowledge management strategies.
The framework considers upstream, technological, and downstream complexities.
Participant type, strategy objective, and knowledge structure are identified as key determinants.

Conclusions:

The proposed framework provides a foundation for developing effective governance rules for OSDD.
It aims to enhance both knowledge accessibility and the efficiency of drug development pipelines.
This structured approach is crucial for realizing the full potential of open source in pharmaceutical innovation.