Imbalanced data, where certain classes are significantly underrepresented in a dataset, is a widespread machine learning (ML) challenge across various fields of chemistry, yet it remains inadequately addressed. This data imbalance can lead to biased ML or deep learning (DL) models, which fail to accurately predict the underrepresented classes, thus limiting the robustness and applicability of these models. With the rapid advancement of ML and DL algorithms, several promising solutions to this issue have emerged, prompting the need for a comprehensive review of current methodologies. In this review, we examine the prominent ML approaches used to tackle the imbalanced data challenge in different areas of chemistry, including resampling techniques, data augmentation techniques, algorithmic approaches, and feature engineering strategies. Each of these methods is evaluated in the context of its application across various aspects of chemistry, such as drug discovery, materials science, cheminformatics, and catalysis. We also explore future directions for overcoming the imbalanced data challenge and emphasize data augmentation via physical models, large language models (LLMs), and advanced mathematics. The benefit of balanced data in new material design and production and the persistent challenges are discussed. Overall, this review aims to elucidate the prevalent ML techniques applied to mitigate the impacts of imbalanced data within the field of chemistry and offer insights into future directions for research and application.

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

A transformer is the foundational architecture behind large language models designed to handle sequential data by using mechanisms of self‐attention to weigh the importance of different elements, enabling efficient processing and understanding of complex patterns. Recently, transformer‐based models have become some of the most popular and powerful deep learning (DL) algorithms in molecular science, owing to their distinctive architectural characteristics and proficiency in handling intricate data. These models leverage the capacity of transformer architectures to capture complex hierarchical dependencies within sequential data. As the applications of transformers in molecular science are very widespread, in this review, we only focus on the technical aspects of transformer technology in molecule domain. Specifically, we will provide an in‐depth investigation into the algorithms of transformer‐based machine learning techniques in molecular science. The models under consideration include generative pre‐trained transformer (GPT), bidirectional and auto‐regressive transformers (BART), bidirectional encoder representations from transformers (BERT), graph transformer, transformer‐XL, text‐to‐text transfer transformer, vision transformers (ViT), detection transformer (DETR), conformer, contrastive language‐image pre‐training (CLIP), sparse transformers, and mobile and efficient transformers. By examining the inner workings of these models, we aim to elucidate how their architectural innovations contribute to their effectiveness in processing complex molecular data. We will also discuss promising trends in transformer models within the context of molecular science, emphasizing their technical capabilities and potential for interdisciplinary research. This review seeks to provide a comprehensive understanding of the transformer‐based machine learning techniques that are driving advancements in molecular science.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

Topological data analysis (TDA) has emerged as a powerful framework for extracting robust, multiscale, and interpretable features from complex molecular data for artificial intelligence (AI) modeling and topological deep learning (TDL). This review provides a comprehensive overview of the development, methodologies, and applications of TDA in molecular sciences. We trace the evolution of TDA from early qualitative tools to advanced quantitative and predictive models, highlighting innovations such as persistent homology, persistent Laplacians, and topological machine learning. The paper explores TDA's transformative impact across diverse domains, including biomolecular stability, protein-ligand interactions, drug discovery, materials science, topological sequence analysis, and viral evolution. Special attention is paid to recent advances in integrating TDA with machine learning and AI, enabling breakthroughs in protein engineering, solubility, and toxicity prediction, and the discovery of novel materials and therapeutics. We also discuss the limitations of current TDA approaches and outline future directions, including the integration of TDA with advanced AI models and the development of new topological invariants. This review aims to serve as a foundational reference for researchers seeking to harness the power of topology in molecular sciences.