Developing a new drug from an idea to a successful product is a complex process that can take up to 20 years and cost more than €1 billion. The early drug discovery and development process is a massive search function for remedies that demonstrate desirable changes in a biological system with minimum negative impact. It is a highly imperfect science, yet crucial for guiding the early concept of a biologically active molecule to a marketed pharmaceutical product.
Modern pharmaceutical companies are measured by the size of their compound libraries – these millions of compounds are initially screened in silico to generate hundreds or thousands of hits, hits are then optimized into promising lead compounds, leads are screened in vitro and ex vivo to optimize properties, followed by the ultimate step of clinical trials. This process is known as a Drug Discovery Funnel.
The attrition of this process is astronomical, where hundreds of thousands or millions of compounds generate one clinical trial drug candidate. These candidates fail during clinical trials more often than not, mostly due to poor translational research models and inappropriate in vivo disease models.
Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. SBDD is nowadays central to the efficient development of therapeutic agents and the understanding of metabolic processes. SBDD is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of disease and utilizes the knowledge of the three-dimensional (3D) structure of the biological target in the process.
State of the art structure-based drug design methods include virtual screening (VS) and de novo drug design; these serve as an efficient, alternative approach to HTS. In virtual screening, large libraries of drug-like compounds that are commercially available are computationally screened against targets of known structure, and those that are predicted to bind well are experimentally tested.
In the de novo drug design approach, the 3D structure of the receptor is used to design structurally novel molecules that have never been synthesized before using ligand-growing programs and the intuition of the medicinal chemist
Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. High-throughput virtual screening (HTVS) is a VS technique where search libraries count from a few thousand to a few million compounds, screened against one or more targets. HTVS requires a powerful computational engine to perform screening in a time-efficient manner, as well as extensive pre-processing and post-processing tools.
Computational informatics – or bioinformatics – 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.
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.
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.
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 project failed to give desired results or stopped their development after lead scientists retired or took other projects.
Paradoxically, the need for an 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 such research and provide extensive training to their students or employees and – either secure licensing for proprietary software or make do with unoptimized software of yore.
We at RxTx choose neither of these options. We decided to base our products on open source from the ground up.