Tuesday, June 26st, Rockville, MD - Today, Insilico Medicine, Inc., a Rockville-based next-generation artificial intelligence company specializing in the application of deep learning for target identification, drug discovery and aging research announces the publication of a new research paper "Reinforced Adversarial Neural Computer for De Novo Molecular Design" in The Journal of Chemical Information and Modeling.
The authors presented an original deep neural network architecture named Reinforced Adversarial Neural Computer (RANC) for the de novo design of novel small-molecule organic structures utilizing the generative adversarial network (GAN) and reinforcement learning (RL) methods.
Although computer-aided approaches in this field are well-studied, the application of deep learning methods in this research area is at the beginning facing a lot of challenges.
The comparative results have shown that RANC trained on the SMILES string representation of the molecules outperforms the other methods by several metrics relevant to drug discovery: the number of unique structures, passing medicinal chemistry filters, Muegge criteria and high quantitative estimate of drug-likeness scores.
MW, logP, TPSA) and lengths of the SMILES strings in the training dataset.
Therefore, RANC can be reasonably regarded as a promising starting point to develop novel molecules with activity against different biological targets or pathways.