Summary of Accelerating Drug Safety Assessment Using Bidirectional-lstm For Smiles Data, by K. Venkateswara Rao et al.
Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Data
by K. Venkateswara Rao, Kunjam Nageswara Rao, G. Sita Ratnam
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents a novel computational approach for predicting toxicity and solubility in lead compounds using Simplified Molecular Input Line Entry System (SMILES) notation. The method leverages a sequence-based Bi-Directional Long Short Term Memory (BiLSTM) model, which processes input molecular sequences to identify structural features from both forward and backward directions. This approach aims to uncover sequential patterns encoded in SMILES strings for toxicity prediction on the ClinTox dataset, outperforming previous methods such as Trimnet and Pre-training Graph neural networks(GNN). Additionally, the BiLSTM model achieves a low RMSE value of 1.22 on the FreeSolv dataset for solubility prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to help find new medicines faster. It works by looking at special codes called SMILES strings that describe molecules. The researchers created a new way to use these codes, called BiLSTM, which can predict how well a medicine will work and what might happen if it’s taken. They tested this method on two big datasets and found it worked better than other methods. This could be very helpful for scientists trying to find new medicines. |
Keywords
* Artificial intelligence * Gnn
