Towards reproducible computational drug discovery

Abstract

The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.

Keywords: Bioinformatics; Cheminformatics; Data science; Data sharing; Drug design; Drug discovery; Open data; Open science; Reproducibility; Reproducible research.

Fig. 1

Schematic summary of the drug discovery process overlayed with corresponding computational approaches

Fig. 2

Conceptual map on the experimental and computational methodologies as applied to the drug discovery process [283]. The ordering of terminologies on each of the colored tracks are not of any specific order

Fig. 3

Number of articles on PubMed, mentioning “Pipeline Pilot” or “KNIME” in their title or abstract from 2003 to 2017

Fig. 4

Schematic comparison of virtual machines and containers. Virtual machines run on a Hypervisor and contains their own Guest Operating System. In contrast, Containers provide a layer of isolation that share the Host Operating System kernel and are hence smaller and faster to instantiate than virtual machines

Fig. 5

A comparison between monolith services and microservices. In traditional services (left), each service consists of a monolithic implementation that encapsulates all necessary components under a single interface. In contrast, a Microservice-based implementation (right) has the individual components that make up an exposed service running independently, making it easier to scale parts of the service if needed as well as offering the benefit of reusing sub-components in other settings