  Deep learning methods have permeated into the research area of computer-aided
drug design. The deep learning generative model and classical algorithm can be
simultaneously used for three-dimensional (3D) drug design in the 3D pocket of
the receptor. Here, three aspects of MolAICal are illustrated for drug design:
in the first part, the MolAICal uses the genetic algorithm, Vinardo score and
deep learning generative model trained by generative adversarial net (GAN) for
drug design. In the second part, the deep learning generative model is trained
by drug-like molecules from the drug database such as ZINC database. The
MolAICal invokes the deep learning generative model and molecular docking for
drug virtual screening automatically. In the third part, the useful drug tools
are added for calculating the relative properties such as Pan-assay
interference compounds (PAINS), Lipinski's rule of five, synthetic
accessibility (SA), and so on. Besides, the structural similarity search and
quantitative structure-activity relationship (QSAR), etc are also embedded for
the calculations of drug properties in the MolAICal. MolAICal will constantly
optimize and develop the current and new modules for drug design. The MolAICal
can help the scientists, pharmacists and biologists to design the rational 3D
drugs in the receptor pocket through the deep learning model and classical
programming. MolAICal is free of charge for any academic and educational
purposes, and it can be downloaded from the website https://molaical.github.io.
