"Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice," said Insilico Medicine in a paper published in the journal Nature Biotechnology.
In comparison, traditional drug discovery starts with the testing of thousands of small molecules in order to get to just a few lead-like molecules and only about one in 10 of these molecules pass clinical trials in human patients.
In a similar technique used by DeepMind to outcompete human GO players, GENTRL -- powered by generative chemistry that utilizes modern AI techniques -- can rapidly generate novel molecular structures with specified properties.
Insilico has also made GENTRL's source code available as open source on Microsoft-owned repository GitHub.
"The development of these first six molecules as an experimental validation is just the start," said Alex Zhavoronkov, CEO of Insilico Medicine.
"By enabling the rapid discovery of novel molecules and by making GENTRL's source code open source, we are ushering in new possibilities for the creation and discovery of new life-saving medicine for incurable diseases," he added.
The new technology leveraged Insilico's groundbreaking academic research in 2016 about using modern AI techniques of generative adversarial networks (GAN) and generative reinforcement learning (RL) to accelerate drug discovery.
"When we first proposed the idea of using the AI technique of generative adversarial networks to accelerate drug discovery in 2016, most of the industry was sceptical," said Zhavoronkov.
The creation of the new molecules marks the industry's first scientific validation of using of generative and reinforcement learning AI technologies for the successful discovery and generation of new molecules.
Insilico Medicine is developing a comprehensive drug discovery pipeline utilizing AI generating novel molecules with the specified properties for a variety of target classes.
"This paper is certainly a really impressive advance and likely to be applicable to many other problems in drug-design," said Dr Michael Levitt, professor of structural biology, Stanford University who received the Nobel Prize in Chemistry in 2013.