Deep learning models have demonstrated outstanding results in many data-rich areas of
research, such as computer vision and natural language processing. Currently, there is a …

In recent years, deep learning-based methods have emerged as promising tools for de novo
drug design. Most of these methods are ligand-based, where an initial target-specific ligand …

Advancements in neural machinery have led to a wide range of algorithmic solutions for
molecular property prediction. Two classes of models in particular have yielded promising …

Molecular optimization aims to improve the drug profile of a starting molecule. It is a
fundamental problem in drug discovery but challenging due to (i) the requirement of …

Challenges in natural sciences can often be phrased as optimization problems. Machine
learning techniques have recently been applied to solve such problems. One example in …

There has been a wave of generative models for molecules triggered by advances in the
field of Deep Learning. These generative models are often used to optimize chemical …

In recent decades, artificial intelligence and machine learning have played a significant role
in increasing the efficiency of processes across a wide spectrum of industries. When it …

Deep learning is transforming many areas in science, and it has great potential in modeling
molecular systems. However, unlike the mature deployment of deep learning in computer …

A new framework to automate, augment, and accelerate steps in computer‐aided molecular
design is presented. The problem is tackled in three stages:(1) composition design,(2) …

Highlights•Quantum computing and its use for computer-aided product design is discussed
and perspectives for several types of problems are provided.•Hybrid QC methods are likely …