Abstract Large Language Models (LLMs) have demonstrated remarkable reasoning
abilities, prompting interest in their application as black-box optimizers. This paper asserts …

A latent factor analysis (LFA) model can be efficiently built via the stochastic gradient
descent (SGD) algorithm to address high-dimensional and incomplete (HDI) data generated …

1. ABSTRACT Natural Language Processing (NLP) has been taking center stage in this field
of computational linguistics, which is focused on the realization that machines can effectively …

Transformer-based large language models have remarkable potential to accelerate design
optimization for applications such as drug development and material discovery. Self …

The inverse design of novel molecules with a desirable optoelectronic property requires
consideration of the vast chemical spaces associated with varying chemical composition …

In this study, we introduce semi-supervised machine learning models designed to predict
molecular properties. Our model employs a two-stage approach, involving pre-training and …

The vast size of chemical space necessitates computational approaches to automate and
accelerate the design of molecular sequences to guide experimental efforts for drug …

Molecular discovery is central to the field of chemical informatics. Although optimization
approaches have been developed that target-specific molecular properties in combination …

Drug discovery is an expensive and risky process. To combat the challenges in drug
discovery, an increasing number of researchers and pharmaceutical companies recognize …

Neural Architecture Search (NAS) has emerged as one of the effective methods to design
the optimal neural network architecture automatically. Although neural architectures have …