Bigger data, collaborative tools and the future of predictive drug discovery

Abstract

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service (SaaS) commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

Automated discovery of GPCR bioactive ligands

“…In recent years, machine learning has become one of the most widely used approaches in drug discovery and development [31, [41][42][43][44][45][46][47]. Often, machine learning is combined with structure-based, ligand-based, and high-throughput screening to automate QSAR-based target prioritization in iterative and automated or semi-automated virtual screening pipelines ( Figure 2).…”

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

“…We and others have recently described [15] privacy concerns with data and efforts to find data-sharing methods as well as examples of companies comparing their compound libraries (e.g., Bayer and Schering [102], Bayer and AstraZeneca [103] and Pfizer to the literature [s7] [104]). Published efforts have also been reviewed on sharing relevant chemical information about screening data that leave structures blinded, which could open the door for increased collaboration.…”

“…We have highlighted the need for more-competitive collaboration in the industry [12] and alternative business models for drug discovery that balance collaboration, privacy and security [13], and certainly the shift toward public–private partnerships (PPPs) fulfils that gap [14]. These in turn will face the challenge of a growing mountain of data and the need for data mining and collaborative tools [15]. …”

App-etite for change

“…There have been more recent other publications [22][23][24][25][26] and a wiki lists some apps of interest [27]. A few examples have been mentioned above.…”