Recurrent Models for Drug Generation
Angélica Santos Carvalho · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2019
Drug discovery aims to identify potential new medicines through a multidisciplinary process, including several scientific areas, such as biology, chemistry and pharmacology.Nowadays, multiple strategies and methodologies have been developed to discover, test and optimise new drugs.However, there is a long process from target identification to an optimal marketable molecule.The main purpose of this dissertation is to develop computational models able to propose new drug compounds.In order to achieve this goal, the artificial neural networks explored and trained to generate new drugs in the form of Simplified Molecular-Input Line-Entry System (SMILES).The explored neural networks model were Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bidirectional Long-Short Term Memory (BLSTM).A consistent dataset was chosen, and the generated SMILES by the model were syntactically and biochemically validated.In order to restrict the generation of SMILES, a technique denominated Fragmentation Growing Procedure was used, where made it possible to choose a fragment and generate SMILES from that.To analyse the recurrent network that fits the best and the respective parameters, some tests were performed, and the network contained in the model that reached the best result, 98% of valid SMILES and 93% of unique SMILES, was an LSTM with two layers.The technique to restrict the generation was used in the best model and reached 99% of valid SMILES and 79% of unique SMILES.