Photo credit: Clay Banks, Unsplash
Bioinformatics (computational informatics) is an interdisciplinary field that develops methods and software tools for understanding biological data. It is a field that was born during a Human Genome Project in the last decade of the 20th century, and it is a field that revolutionized almost every aspect of biomedical science and gave birth to many fields within molecular biology and modern biochemistry.
The main reason for this biomedical Renaissance is the fact that every core aspect of computational biology was open source, every part of the code and algorithms was standardized, and any research group in the world – from scientific giants in the USA to start-ups in Asia or Africa – could contribute to the scientific field, freely collaborate and grow exponentially and without any legal barrier.
Unlike bioinformatics, computational chemistry did not take that path.
History of computational chemistry software toolbox is mired with proprietary software and high-cost entry barrier to anyone starting their medicinal chemistry training, setting up modern drug discovery laboratory in academia, or financing their research as a fledgling CRO. Quite simply put – cutting-edge computational drug discovery tools have a hefty price tag on them.
But that is not the biggest issue. The biggest issue for professional users is the lack of control you have while using proprietary software. Your experiments are run in a black-box kind of environment, where each functionality is properly documented, but the back-end of the software is never revealed nor it can be tinkered with. In an industry where funding is not an issue, but each week, day, or literally hour can mean the difference between landing a product worth dozens of billions of euros or going bust, having complete and utter control over your workflow is often a matter of life and death.
On the other side, a great number of free-to-use computational tools emerged in academia. While it was a step in the right direction, it did not allow collaboration and most of the projects failed to give desired results or stopped their development after lead scientists retired or took up other projects. The lack of control over your workflow is still present, and more often than not the project just dies with the project call funding end, group leaders eventually start to have some other goal or any other similar reason.
Paradoxically, the need for an high-throughput virtual screening (HTVS) toolbox was never greater than today, and scientist across academia and industry face one of two choices – secure a tremendous amount of funding for building or renting infrastructure needed to perform drug discovery virtual pipeline and provide extensive training to their students or employees and – either secure licensing for proprietary software or make do with unoptimized software of yore.
RxDock is a free and open source software (FOSS) for HTVS and it alleviates both of these problems. While it obviously is not using the most advanced algorithms (at least yet) and it is obviously not the most optimized HTVS package compared to the best commercial software (yet), it gives you complete control over the code and your workflow.
Since it is open source by default you can use it for free and start using it immediately via GitLab or GitHub. Unlike academic freeware, you can also tinker with the code, implement the new features in the code, improve and optimize existing features, expand the documentation on the existing feature set, etc. Being a part of the RxDock community is not just about being a passive user of the product with no say in how the software will grow and evolve.
You are an active contributor and an active part of a community that will iterate on the existing version of RxDock and make it better, more feature-rich, and more optimized with each commit. You can also fork your own version of RxDock and make an independent version that will make your vision of the HTVS package come true.
My team at RxTx and me honestly can not wait to see what will the community build around RxDock and how it will be integrated into your workflow. We will be making blog posts about basic usage of RxDock as well as a separate development diary our Scientific Software Architect Vedran Miletić will be writing targeted at developers who would like to start developing for and around RxDock. Feel free to follow RxDock development on GitLab, GitHub, and community forum, and get updates from RxTx on LinkedIn and Twitter accounts. Also, do not hesitate to contact us. We wish you all the best in the present holiday season and we can't wait to see what will 2021 bring to RxDock.