At the beginning of the 2000s, computational techniques became one of the biggest miracles to pursue a drug discovery journey in an expedited fashion. The technique was not new but arguably the brightest era started around these days.
The time passed and many full-fledged software packages, even suits of apps, emerged for a faster, more automated and easier solution. Just, I was not convinced about the idea of apps getting easier. That part was always a question on my mind. I was basically thinking of others who are not computer enthusiasts and have no reason to be. Someone might be a great chemist and just wants to work in her own duties. Why should she need to understand how to create a shell script that could run through all molecule files, in a parallel fashion? Even worse; she may have access to a computer with 128 cores waiting idle, but have no idea why the damn simple docking simulation is taking hours.
The day I started my graduate education for Computational Biology and Bioinformatics I knew that somebody had to fix this mess. I am not sure it is getting easier to use any particular tool to achieve a simple goal. But there are very capable software systems to reach the main goal. Great tools are emerging and software starts to process most of the automation for scientists. For today, I would like to discuss the current open source tools, then I will describe advances in AI research that could eliminate most of the process clearly.
The main objective is here to show how to do computational small molecule discovery with structure-based drug discovery tools. We will start with obtaining the protein structure data, cleaning and preparing it, how to select a scaffold candidate, prepare multiple variants of a ligand, molecular docking for elimination and at the end analyzing the results.