Computational tools for drug discovery: AI-based
| Tools | Purpose | References |
|---|---|---|
| DeepChem | Drug discovery task prediction | [28] |
| DTI-CNN | DL based drug-target interaction prediction | [29] |
| ORGANIC | Molecular generation tool with desired properties | [30] |
| Chemputer | Chemical synthesis reporting procedure | [31] |
| DeltaVina | Rescoring protein-ligand binding affinity: scoring | [32] |
| DeepCPI | Drug–protein interaction prediction | [33] |
| PotentialNet | A CNN graph-based ligand-binding affinity prediction | [34] |
| DeepNeuralNet-QSAR | Prediction of molecular activity | [35] |
| Hit Dexter | Prediction of molecules responding to biochemical assays | [36] |
| DeepTox | For toxicity prediction | [37] |
| PPB2 | Polypharmacology prediction | [38] |
| SCScore | For evaluation of the synthesis complexity of a molecule | [39] |
| NNScore | Protein-ligand interaction scoring study | [40] |
| SIEVE-Score | Structure-based virtual screening | [41] |
| REINVENT | Molecular de novo design based on RNN and RL | [42] |
RL: reinforcement learning; DTI-CNN: drug-target interaction-CNN; QSAR: quantitative structure-activity relationship; PPB2: polypharmacology browser 2; SCScore: synthetic complexity score; SIEVE-Score: similarity of interaction energy vector-score; DeepTox: DL for toxicity; NNScore: neutral-network receptor-ligand scoring function