Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …

Molecular design strategies are integral to therapeutic progress in drug discovery.
Computational approaches for de novo molecular design have been developed over the …

Highlights•Compound enumeration coupled with docking is rapidly gaining
popularity.•Docking-based generative models are reviewed.•New taxonomy for docking …

The efficient exploration of chemical space to design molecules with intended properties
enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most …

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and
Machine Learning (ML) with cheminformatics has proven to be a powerful combination …

Inverse molecular design involves algorithms that sample molecules with specific target
properties from a multitude of candidates and can be posed as an optimization problem …

The simplified molecular-input line-entry system (SMILES) is the most prevalent molecular
representation used in AI-based chemical applications. However, there are innate limitations …

Many deep learning (DL)-based molecular generative models have been proposed to
design novel molecules. These models may perform well on benchmarks, but they usually …

Inverse molecular design, ie, designing molecules with specific target properties, can be
posed as an optimization problem. High-dimensional optimization tasks in the natural …

Deep generative models have become crucial tools in de novo drug design. In current
models for multiobjective optimization in molecular generation, the scaffold diversity is …