Source: Eurekalert
Designing new molecules for pharmaceuticals is primarily a manual, time-consuming process that’s prone to error. But MIT researchers have now taken a step toward fully automating the design process, which could drastically speed things up — and produce better results.
Drug discovery relies on lead optimization. In this process, chemists select a target (“lead”) molecule with known potential to combat a specific disease, then tweak its chemical properties for higher potency and other factors.
Often, chemists use expert knowledge and conduct manual tweaking of molecules, adding and subtracting functional groups — atoms and bonds responsible for specific chemical reactions — one by one. Even if they use systems that predict optimal chemical properties, chemists still need to do each modification step themselves. This can take hours for each iteration and may still not produce a valid drug candidate.
This article highlights the scope for developing therapeutics with new innovative, alternative approaches. The current trillion dollars pharmaceutical industry is in peril where the current drug development pipeline is slow, inefficient and incapable of being extended to multi-combination drug therapies as well as minimally focused on prevention. The complex modeling of diseases and biological functions has been limited because of the inability to integrate large scale molecular pathways.
Watch this Video to understand how recent advances provide breakthrough technology for doing scalable modeling of complex molecular systems to dramatically accelerate drug discovery and development.