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Abstract
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.
Keywords
Drug Discovery, Deep Learning, Recurrent Models, Validation, Fragmentation Growing
Procedure
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