  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.
