Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Data

TL;DR

This paper introduces a bidirectional LSTM model for predicting toxicity and solubility of drug compounds from SMILES data, significantly improving accuracy over previous methods in drug safety assessment.

Contribution

The study presents a novel sequence-based BiLSTM approach for SMILES data that outperforms existing models in toxicity and solubility prediction tasks.

Findings

Achieved ROC of 0.96 on ClinTox dataset

Reduced RMSE to 1.22 in solubility prediction

Outperformed previous models like Trimnet and GNN

Abstract

Computational methods are useful in accelerating the pace of drug discovery. Drug discovery carries several steps such as target identification and validation, lead discovery, and lead optimisation etc., In the phase of lead optimisation, the absorption, distribution, metabolism, excretion, and toxicity properties of lead compounds are assessed. To address the issue of predicting toxicity and solubility in the lead compounds, represented in Simplified Molecular Input Line Entry System (SMILES) notation. Among the different approaches that work on SMILES data, the proposed model was built using a sequence-based approach. The proposed Bi-Directional Long Short Term Memory (BiLSTM) is a variant of Recurrent Neural Network (RNN) that processes input molecular sequences for the comprehensive examination of the structural features of molecules from both forward and backward directions. The proposed work aims to understand the sequential patterns encoded in the SMILES strings, which are then utilised for predicting the toxicity of the molecules. The proposed model on the ClinTox dataset surpasses previous approaches such as Trimnet and Pre-training Graph neural networks(GNN) by achieving a ROC accuracy of 0.96. BiLSTM outperforms the previous model on FreeSolv dataset with a low RMSE value of 1.22 in solubility prediction.