Table 1.
Enumeration of AI-Aided Computational Tools for Facilitating Drug Discovery.
| Tools | Feature(s) | Website(s) | Reference(s) |
|---|---|---|---|
| AlphaFold | Protein 3D (tertiary) structure presage employing DNN |
https://deepmind.com/blog/alphafold (accessed on 28 November 2022) https://www.sciencemag.org/news/2018/12/google-s-deepmind-aces-protein-folding (accessed on 28 November 2022) |
[151] |
| Chemputer | An exhaustive regulated schema for documenting a chemical synthesis method (Furnishes comprehensive compound synthesis recipe) |
https://zenodo.org/record/1481731 (accessed on 28 November 2022) |
[149] |
| Conv_qsar_fast | Foretells molecular attributes aided by CNN algorithm |
https://github.com/connorcoley/conv_qsar_fast (accessed on 28 November 2022) |
[130] |
| Chemical VAE | Mechanized chemical crafting employing variational autoencoder (VAE) |
https://github.com/aspuru-guzik-group/chemical_vae (accessed on 28 November 2022) |
[133] |
| DeepChem | A Python-aided AI technique for various drug discovery workflow predictions utilizing a DL algorithm for molecule recognition |
https://github.com/deepchem/deepchem (accessed on 28 November 2022) |
[152] |
| DeepNeuralNet-QSAR | Foretells molecular activity engaging multilevel DNN |
https://github.com/Merck/DeepNeuralNet-QSAR (accessed on 28 November 2022) |
[153] |
| DeepTox | Toxicity predictions of chemical agents utilizing a DL algorithm |
www.bioinf.jku.at/research/DeepTox (accessed on 28 November 2022) |
[154] |
| DeltaVina | Presages small molecule interaction affinity with drug employing an amalgamation of random forest (RF) as well as AutoDock scoring function) |
https://github.com/chengwang88/deltavina (accessed on 28 November 2022) |
[111] |
| Hit Dexter | ML schemes for the presage of compounds that could be sensitive to biochemical assays by engaging ML techniques |
http://hitdexter2.zbh.uni-hamburg.de (accessed on 28 November 2022) |
[155] |
| InnerOuterRNN | Foretells the chemical, physical, and biological attributes utilizing inner- and outer RNNs |
https://github.com/Chemoinformatics/InnerOuterRNN (accessed on 28 November 2022) |
[156] |
| JunctionTree VAE | De novo molecule origination utilizing junction tree variational autoencoder (VAE) |
https://github.com/wengong-jin/icml18-jtnn (accessed on 28 November 2022) |
[157] |
| Neural Graph Fingerprints | Attribute augury of novel molecules employing CNN algorithms |
https://github.com/HIPS/neural-fingerprint (accessed on 28 November 2022) |
[158] |
| NNScore | Foretells the affinity of protein–ligand binding utilizing neural network-aided scoring function |
http://rocce-vm0.ucsd.edu/data/sw/hosted/nnscore/ (accessed on 28 November 2022) http://www.nbcr.net/software/nnscore (accessed on 28 November 2022) |
[159] |
| Open Drug Discovery Toolkit (ODDT) | An exhaustive toolkit utilized for chemoinformatics and molecular modelling employing random forest score (RF)-Score as well as NNScore |
https://github.com/oddt/oddt (accessed on 28 November 2022) |
[160] |
| ORGANIC | A competent molecular generation tool to originate molecules with favourable attributes employing ML schemes |
https://github.com/aspuru-guzik-group/ORGANIC (accessed on 28 November 2022) |
[161] |
| PotentialNet | Foretells ligand-binding affinity engaging graph CNN |
https://pubs.acs.org/doi/full/10.1021/acscentsci.8b00507 (accessed on 28 November 2022) |
[162] |
| PPB2 | Poly-pharmacology prediction employing nearest neighbour as well as ML schemes |
http://ppb2.gdb.tools/ (accessed on 28 November 2022) |
[163] |
| QML | A Python toolkit for quantum ML (utilizing qubits leading to incremented computational speed, data storage capacity, and learning optimization) |
https://www.qmlcode.org (accessed on 28 November 2022) https://github.com/qmlcode/qm (accessed on 28 November 2022) |
[164] |
| REINVENT | De novo design of molecule employing RNN (recurrent neural network) as well as RL (reinforcement learning) |
https://github.com/MarcusOlivecrona/REINVENT (accessed on 28 November 2022) |
[165] |
| SCScore | A scoring scheme to figure out the synthesis complexity of a compound |
https://github.com/connorcoley/scscore (accessed on 28 November 2022) |
[166] |
| SIEVE-Score | An upgraded technique of structure-aided virtual screening through interaction-energy-based learning |
https://github.com/sekijima-lab/SIEVE-Score (accessed on 28 November 2022) |
[167] |