Theoretical and applied potential of artificial intelligence and machine learning in analysing molecular data

Year 2025, , 61 - 70, 31.01.2025

Fatih Mehmet Avcu

https://doi.org/10.51435/turkjac.1607205

Abstract

This article examines the theoretical potential and applications of artificial intelligence (AI) and machine learning (ML) in molecular analysis. AI and ML techniques allow accelerating and improving the accuracy of chemical and biological processes. In particular, these methods are used to predict the chemical structure, biological activity and protein structure of molecules. In this article, we discuss how various data types such as molecular dynamics simulations, spectroscopy and cheminformatics data can be processed with AI and ML algorithms. It also highlights the revolutionary contributions of deep learning algorithms in areas such as molecular representations, drug design and protein structure prediction. The effectiveness of reinforcement learning and graph-based models in the prediction and optimization of chemical reactions is also discussed. In conclusion, the use of AI and ML in molecular analyses is expected to expand into broader areas of scientific and industrial research in the future.

Keywords

Artificial Intelligence (AI), Machine Learning (ML), Molecular Analysis, Deep Learning, Chemical Reactions

Theoretical and applied potential of artificial intelligence and machine learning in analysing molecular data

Abstract

Bu makale, moleküler analizde yapay zeka (AI) ve makine öğreniminin (ML) teorik potansiyelini ve uygulamalarını incelemektedir. YZ ve makine öğrenimi teknikleri, kimyasal ve biyolojik süreçlerin doğruluğunu hızlandırmaya ve iyileştirmeye olanak tanır. Özellikle, bu yöntemler moleküllerin kimyasal yapısını, biyolojik aktivitesini ve protein yapısını tahmin etmek için kullanılır. Bu makalede, moleküler dinamik simülasyonları, spektroskopi ve kemoinformatik verileri gibi çeşitli veri türlerinin yapay zeka ve makine öğrenimi algoritmaları ile nasıl işlenebileceği tartışılmaktadır. Ayrıca derin öğrenme algoritmalarının moleküler temsiller, ilaç tasarımı ve protein yapısı tahmini gibi alanlardaki devrim niteliğindeki katkıları vurgulanmaktadır. Kimyasal reaksiyonların tahmini ve optimizasyonunda takviyeli öğrenme ve grafik tabanlı modellerin etkinliği de tartışılmaktadır. Sonuç olarak, AI ve ML'nin moleküler analizlerde kullanımının gelecekte daha geniş bilimsel ve endüstriyel araştırma alanlarına yayılması beklenmektedir.

Keywords

Yapay Zeka (AI), Makine Öğrenimi (ML), Moleküler Analiz, Derin Öğrenme, Kimyasal Reaksiyonlar

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