Dear Colleagues,

The exploration of chemical space is one of the most important tasks in contemporary cheminformatics research. In recent years, deep generative models have attracted a significant amount of attention and are slowly becoming an integral part of workflows for ligand-based drug or chemical probe discovery. Many aspects of deep generators have been extensively studied. These include various deep neural network architectures (e.g., convolutional and recurrent networks or autoencoders), different learning strategies (e.g., adversarial, transfer, or reinforcement learning), or representations of the generated molecules (e.g., SMILES, alternative string representations, or graphs). Various issues, such as the assessment of generated molecule biological significance or the interpretability of deep models, remain to be solved. In addition, although today’s research strongly emphasizes deep learning models, they still can be successfully completed by more traditional methods, such as genetic algorithms or molecular-fragment-based generators.

This Special Issue is focused on in silico methods for the design of new chemical structures, and it will include original articles as well as reviews on all aspects related to the use of artificial intelligence methods (e.g., deep neural networks, genetic algorithms, and others) for molecular generation.

Prof. Dr. Daniel Svozil
Guest Editor

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• molecular generation
• deep learning
• generative models
• artificial intelligence
• neural networks
• chemical space
• drug discovery