De novo design seeks to generate molecules with required property profiles by virtual
design-make-test cycles. With the emergence of deep learning and neural generative …

Research productivity in the pharmaceutical industry has declined significantly in recent
decades, with higher costs, longer timelines, and lower success rates of drug candidates in …

In the past few years, de novo molecular design has increasingly been using generative
models from the emergent field of Deep Learning, proposing novel compounds that are …

This work introduces a method to tune a sequence-based generative model for molecular de
novo design that through augmented episodic likelihood can learn to generate structures …

De novo drug design aims to generate novel chemical compounds with desirable chemical
and pharmacological properties from scratch using computer-based methods. Recently …

Automatic design of organic materials requires black-box optimization in a vast chemical
space. In conventional molecular design algorithms, a molecule is built as a combination of …

The discovery of functional molecules is an expensive and time-consuming process,
exemplified by the rising costs of small molecule therapeutic discovery. One class of …

Machine learning provides effective computational tools for exploring the chemical space via
deep generative models. Here, we propose a new reinforcement learning scheme to fine …

A plethora of AI-based techniques now exists to conduct de novo molecule generation that
can devise molecules conditioned towards a particular endpoint in the context of drug …

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