Deep generative models like diffusion models have generated significant interest in
computer-aided molecular design by enabling the automated generation of novel molecular …

Automatically extracting information from unstructured text in pharmaceutical documents is
important for drug discovery and development. This information can be integrated with …

Data-driven materials science has realized a new paradigm by integrating materials domain
knowledge and machine-learning (ML) techniques. However, ML-based research has often …

Abstract Models, representing a system under study with respect to problems such as
process design, process control, product synthesis and many more, are at the core of most …

The startling success of ChatGPT and other large language models (LLMs) using
transformer-based generative neural network architecture in applications such as natural …

Process flowsheet synthesis, design, and simulation require integrated approaches that
combine domain knowledge and data-driven methods for fast, efficient, and reliable …

Artificial intelligence and machine learning are revolutionising fields of science and
engineering. In recent years, process engineering has widely benefited from this novel …

Abstract Technologies based on Artificial Intelligence (AI) and Machine Learning (ML)
concepts are advancing at a rapid pace. The new paradigms are challenging the status-quo …

This work introduced a scalable and integrated machine learning (ML) framework to
facilitate important steps of building quantitative structure–property relationship (QSPR) …

Computer-aided molecular design (CAMD) studies quantitative structure–property
relationships and discovers desired molecules using optimization algorithms. With the …