Chapter12 : Deep Learning Applied to Ligand-Based De Novo Drug Design

reading notes of《Artificial Intelligence in Drug Design》

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1.Introduction

• One of the big advantages of de novo design is raising the bar in terms of numbers of molecules virtually accessible during a drug discovery project.
• The recent advancements of deep learning techniques allowed the incorporation of the three aforementioned objectives of de novo design into a single multi- objectives optimization step.

2.De Novo Design:History and Background

• GRID and HSITE/SpaceSkeleton were two of the first developed algorithms able to map cavities of proteins in order to identify potential ligand interaction points.
• Following a similar approach but with an higher level of automation, LUDI became a very popular method and it was also included into commercially available modeling software.
• Among the early ligand-based de novo design approaches, it is worthwhile to mention LeapFrog and SPROUT. SPROUT was probably one of the first approaches that included a specific algorithm named Computer Assisted Estimation of Synthetic Accessibility to address the syn- thetic feasibility of novel designed compounds.
• Schneider and coworkers implemented a de novo design method named Design of Genuine Structures (DOGS). In this approach, molecules were generated using a set of about 25,000 available synthetic building blocks and 58 established reaction schemes.

3.Neural Network Architectures for De Novo Design

• Among the several different autoencoder networks present in the literature, it is worth mentioning also Semi-Supervised VAE (SSVAE). In this model, the loss function is based not only on the ability of molecular reconstruction as in the VAE case, but it also considers the prediction accuracy of some molecular properties, such as molecular weight and solubility. In this way, the trained SSVAE network can generate novel compounds with the desired properties.
• Due to its peculiar architecture, GAN presents some intrinsic limitations and weaknesses, such as unbalanced and instable training, and restricted chemical space learnt.
• As molecular representations, SMILES, SELFIES, and graph can be directly employed with GANs. Very recently, also morphological profile images or gene expressions have been used in combination with GAN networks.
• Other less common neural network architectures are Reinforced Adversarial Neural Computer (RANC) and its extension, Adversarial Threshold Neural Computer (ATNC).
• Several different methods have been published to guide the optimization toward a desired chemical space . In this regard, it is worth mentioning Reinforcement Learning (RL), Transfer Learning (TL), Bayesian Optimization (BO),Conditional Gen- erative model (CGM), and Genetic Algorithm (GA).
• In Table 1(p284), we list the currently available deep generative models applied to drug-like molecule generation.