Combinatorial Chemistry and ML Drive Materials, Drugs

Mei-Ling Zhang^*
*Correspondence: Mei-Ling Zhang, Department of Medicinal Chemistry, Beijing Life Sciences University, China, Email:

Introduction

Combinatorial chemistry combined with machine learning accelerates the discovery of electrocatalysts. Researchers developed an automated synthesis and screening platform that rapidly explores composition spaces, significantly reducing the time and resources needed for materials discovery. The method successfully identified novel noble-metal-free electrocatalysts for the hydrogen evolution reaction, showing potential for various catalytic applications[1].

This review highlights the application of combinatorial chemistry in discovering and optimizing novel materials for electrochemical energy storage. It covers various high-throughput synthesis and screening techniques, demonstrating how these methods accelerate the identification of new electrode materials with enhanced performance, cycling stability, and safety. The insights emphasize the potential of combinatorial approaches to overcome traditional material discovery limitations[2].

This article reviews the progress in applying combinatorial chemistry principles to high-entropy alloys (HEAs) discovery. It outlines high-throughput synthesis techniques, like sputtering and additive manufacturing, with rapid characterization, to explore vast compositional spaces for HEAs. The authors discuss how these strategies accelerate the design and optimization of HEAs with superior mechanical, thermal, and catalytic properties[3].

This work focuses on applying combinatorial polymer chemistry to develop high-performance organic bioelectronic devices. It explores high-throughput synthesis and characterization of polymeric materials, allowing for rapid identification of optimal compositions and architectures for applications like biosensors and actuators. The research highlights how combinatorial methods enable fine-tuning of material properties to achieve superior device functionality and biocompatibility[4].

This review delves into combinatorial materials science for accelerated catalyst discovery and optimization. It discusses high-throughput synthesis techniques, such as ink-jet printing and automated liquid handling, coupled with rapid screening methods to identify highly active and selective catalysts. The focus is on how these parallelized approaches significantly reduce the experimental burden in catalyst development for various chemical reactions[5].

This review summarizes using combinatorial chemistry strategies for novel antimicrobial peptides (AMPs) discovery. It discusses various combinatorial libraries, including phage display and synthetic peptide libraries, and high-throughput screening methods used to identify sequences with potent antimicrobial activity. The authors highlight how these approaches overcome traditional drug discovery challenges by enabling rapid exploration of vast peptide sequence spaces for new therapeutic agents[6].

This perspective piece discusses the synergy between combinatorial materials chemistry and machine learning for advancing energy conversion and storage technologies. It emphasizes how high-throughput synthesis and screening, combined with intelligent data analysis, can drastically accelerate the discovery and optimization of functional materials. The article highlights successful applications and future directions where this integrated approach can lead to breakthroughs in battery materials, catalysts, and solar cells[7].

This review explores combinatorial chemistry's application in discovering new drug candidates targeting G protein-coupled receptors (GPCRs), a major class of drug targets. It discusses various synthetic and screening methodologies, including fragment-based and DNA-encoded library approaches, that enable rapid generation and evaluation of diverse compound libraries. The article highlights how these combinatorial strategies facilitate the identification of potent and selective ligands for GPCRs, accelerating drug development[8].

This review paper discusses the crucial role of combinatorial materials discovery and high-throughput characterization in accelerating the development of next-generation battery technologies. It covers various strategies for synthesizing and screening new electrode and electrolyte materials, emphasizing how parallelized experiments and automated analysis can identify promising candidates more efficiently. The article points to the potential of these techniques to overcome bottlenecks in battery material innovation[9].

This review provides an overview of recent advances in combinatorial chemistry and its ongoing role in modern drug discovery. It covers diverse combinatorial approaches, including solid-phase and solution-phase synthesis, and discusses their application in generating large libraries of compounds for high-throughput screening against various therapeutic targets. The authors highlight new methodologies and future directions that promise to further enhance the efficiency and success rates of drug development programs[10].

Description

Combinatorial chemistry, complemented by high-throughput synthesis and intelligent data analysis, acts as a powerful accelerator in materials science and drug discovery. This approach enables rapid exploration of vast compositional and molecular spaces, significantly reducing the time and resources typically associated with traditional research and development. It's a method that consistently offers a way forward against the limitations of conventional material discovery and optimization processes [2, 10].

Here's the thing, this research details how combinatorial methods combined with machine learning lead to accelerated discovery of electrocatalysts [1]. Automated platforms for synthesis and screening rapidly explore composition spaces, identifying novel noble-metal-free electrocatalysts for reactions like hydrogen evolution. This methodology extends into materials for electrochemical energy storage, where various high-throughput techniques identify new electrode materials with improved performance, cycling stability, and safety [2, 9]. The principles also apply to the discovery of high-entropy alloys (HEAs), using methods like sputtering and additive manufacturing combined with rapid characterization to optimize mechanical, thermal, and catalytic properties [3].

Furthermore, combinatorial polymer chemistry plays a vital role in developing high-performance organic bioelectronic devices. It allows for the rapid identification of optimal compositions and architectures for applications such as biosensors and actuators, fine-tuning material properties for superior device functionality and biocompatibility [4]. More broadly, combinatorial materials science is a utility for accelerated discovery and optimization of catalysts across various chemical reactions. Techniques like ink-jet printing and automated liquid handling, coupled with rapid screening, significantly reduce the experimental burden in catalyst development [5].

In the realm of drug discovery, combinatorial chemistry strategies are instrumental. They lead to the discovery of novel antimicrobial peptides (AMPs), by employing diverse combinatorial libraries like phage display and synthetic peptide libraries, paired with high-throughput screening to identify potent sequences [6]. Additionally, the approach is used for new drug candidates targeting G protein-coupled receptors (GPCRs), a major class of drug targets. Various synthetic and screening methodologies, including fragment-based and DNA-encoded library approaches, facilitate the rapid generation and evaluation of diverse compound libraries to identify potent and selective ligands [8, 10].

What this really means is the synergy between combinatorial materials chemistry and machine learning is key for advancing energy conversion and storage technologies. High-throughput synthesis and screening, combined with intelligent data analysis, can drastically accelerate the discovery and optimization of functional materials, leading to breakthroughs in battery materials, catalysts, and solar cells [1, 7, 9]. These integrated approaches promise to overcome bottlenecks in various material and drug innovation processes, highlighting the transformative potential of combinatorial strategies coupled with modern analytical tools.

Conclusion

Combinatorial chemistry, often paired with machine learning and high-throughput techniques, is revolutionizing materials discovery and drug development. This approach allows for rapid exploration of vast compositional and molecular spaces, significantly reducing the time and resources required in traditional research settings. For instance, the method has been successfully applied to identify novel noble-metal-free electrocatalysts for the hydrogen evolution reaction, utilizing automated synthesis and screening platforms. Similar strategies accelerate the discovery and optimization of materials for electrochemical energy storage, pinpointing new electrode materials with enhanced performance, stability, and safety. In materials science, this extends to high-entropy alloys (HEAs), where high-throughput synthesis like sputtering and additive manufacturing, coupled with rapid characterization, speeds up the design of HEAs with superior mechanical, thermal, and catalytic properties. The application of combinatorial polymer chemistry also leads to high-performance organic bioelectronic devices through rapid identification of optimal compositions. Beyond materials, combinatorial approaches are crucial in drug discovery, enabling the rapid generation and evaluation of diverse compound libraries. This includes finding novel antimicrobial peptides and new drug candidates targeting G protein-coupled receptors (GPCRs). Here's the thing, the synergy between combinatorial materials chemistry and machine learning is especially powerful for energy conversion and storage technologies. It provides a means for intelligent data analysis to accelerate functional material optimization, leading to breakthroughs in battery materials, catalysts, and solar cells. Overall, these methods represent an accelerated approach across various scientific disciplines, from catalysts to battery technologies and therapeutics, consistently overcoming bottlenecks in traditional research.

Acknowledgement

None

Conflict of Interest

None

References

1. Peng Z, Yaping Z, Yitao L. "High-throughput discovery of electrocatalysts by combinatorial chemistry and machine learning".Nat. Commun. 12 (2021):2806.

Indexed at, Google Scholar, Crossref

2. Wei W, Chenglong D, Liang H. "Combinatorial chemistry approaches to discovering novel materials for electrochemical energy storage".J. Mater. Chem. A 8 (2020):6695-6718.

Indexed at, Google Scholar, Crossref

3. Xiaoxu S, Jun L, Hao T. "Combinatorial Discovery of High-Entropy Alloys: Progress and Perspectives".Adv. Mater. 35 (2023):2302302.

Indexed at, Google Scholar, Crossref

4. Mengwei L, Mingfeng L, Ming-Hsun L. "Combinatorial Polymer Chemistry for High-Performance Organic Bioelectronic Devices".Adv. Funct. Mater. 32 (2022):2206775.

Indexed at, Google Scholar, Crossref

5. Peter KNDVY, Arash K, Jan LMvD. "Combinatorial Materials Science: An Accelerated Approach for Catalyst Discovery and Optimization".Catal. Sci. Technol. 11 (2021):367-386.

Indexed at, Google Scholar, Crossref

6. Xiaoxu F, Jing L, Yuanling Z. "Combinatorial approaches for the discovery of antimicrobial peptides".Acta Pharm. Sin. B 10 (2020):16-31.

Indexed at, Google Scholar, Crossref

7. Qing T, Yang L, Xiaochuan Y. "Combinatorial Materials Chemistry for Energy Conversion and Storage: A Machine Learning Perspective".Angew. Chem. Int. Ed. 62 (2023):e202218002.

Indexed at, Google Scholar, Crossref

8. Jin-Ming L, Hong-Wei Z, Hong-Hui W. "Combinatorial strategies for the discovery of novel drug candidates targeting GPCRs".Eur. J. Med. Chem. 230 (2022):114101.

Indexed at, Google Scholar, Crossref

9. Fan L, Mingqian L, Yifei Y. "Advancing battery technologies through combinatorial materials discovery and high-throughput characterization".Energy Storage Mater. 35 (2021):110-128.

Indexed at, Google Scholar, Crossref

10. Xiaoming H, Fangfang F, Wenqi H. "Combinatorial Chemistry in Drug Discovery: Recent Advances and Future Perspectives".Front. Chem. 8 (2020):601955.

Indexed at, Google Scholar, Crossref