| scholarly article | Q13442814 |
| P2093 | author name string | Alex Zhavoronkov | |
| Alán Aspuru-Guzik | |||
| Hongming Chen | |||
| Rauf Kurbanov | |||
| Vladimir Aladinskiy | |||
| Sergey Nikolenko | |||
| Simon Johansson | |||
| Alexander Zhebrak | |||
| Artur Kadurin | |||
| Daniil Polykovskiy | |||
| Benjamin Sanchez-Lengeling | |||
| Stanislav Belyaev | |||
| Aleksey Artamonov | |||
| Mark Veselov | |||
| Oktai Tatanov | |||
| Sergey Golovanov | |||
| P2860 | cites work | Virtual Compound Libraries in Computer-Assisted Drug Discovery | Q63953796 |
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| DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures | Q93847384 | ||
| Randomized SMILES strings improve the quality of molecular generative models | Q94204628 | ||
| A de novo molecular generation method using latent vector based generative adversarial network | Q94215940 | ||
| Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions | Q27702095 | ||
| SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules | Q28090714 | ||
| Molecular de-novo design through deep reinforcement learning | Q42766915 | ||
| De Novo Design of Bioactive Small Molecules by Artificial Intelligence. | Q47741086 | ||
| Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks | Q48024105 | ||
| Application of Generative Autoencoder in de Novo Molecular Design | Q48127458 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P304 | page(s) | 565644 | |
| P577 | publication date | 2020-12-18 | |
| P1433 | published in | Frontiers in Pharmacology | Q2681208 |
| P1476 | title | Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models | |
| P478 | volume | 11 |
| Q115031291 | Artificial intelligence for antiviral drug discovery in low resourced settings: A perspective |
| Q114677105 | Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study |
| Q118248060 | Deep generative models for peptide design |
| Q111521194 | Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning |
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