Towards a Machine-Learning-Based Application for Amorphous Drug Recognition

Authors

Keywords:

drug amorphization, monte-carlo method, deep neural network

Abstract

The amorphous drug structure represents an important feature to be reached in the pharmaceutical field due to its possibility of increasing drug solubility, considering that at least 40% of commercially available crystalline drugs are poorly soluble in water. However, it is known that the amorphous local structure can vary depending on the amorphization technique used. Therefore, recognizing such variations related to a specific amorphization technique through the pair distribution function (PDF) method, for example, is an important tool for drug characterization concerns. This work presents a method to classify amorphous drugs according to their amorphization techniques and related to the local structure variations using machine learning. We used experimental PDF patterns obtained from low-energy X-rays scattering data to extract information and expanded the data through the Monte Carlo method to create a synthetic dataset. Then, we proposed the evaluation of such a technique using a Deep Neural Network. Based on the results obtained, it is suggested that the proposed technique is suitable for the amorphization technique and local structure recognition task.

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Author Biographies

Mateus Coelho Silva, Universidade Federal do ABC

Mateus C. Silva is currently an Adjunct Professor in the Center of Mathematics, Computing, and Cognition at the Federal University of ABC (UFABC). He obtained his M.Sc. and Ph.D. in Computer Sciences at the Federal University of Ouro Preto. His current research interests include Machine and Deep Learning, Cyber-Physical Systems, IoT, Wearable Devices, and Robotics. Contact him at mateuscoelho.ccom@gmail.com.

Alcides Castro e Silva, Universidade Federal de Ouro Preto

Alcides Castro e Silva is currently a Full Professor in the Physics Department at the Federal University of Ouro Preto (UFOP). He had a M. Sc. and Ph.D. in Physics at Federal University of Minas Gerais (UFMG). His research interests are Complex Systems and Biological Problems. Lately, he has started to focus on the IA approach to physical problems.

Marcos T. D. Orlando, Universidade Federal do Espírito Santo

Marcos Tadeu d'Azeredo Orlando is currently a Full Professor in the Physics Department at the Federal University of Espírito Santo (UFES). He had a M. Sc. and Ph.D. in Physics at University of Sao Paulo (USP) and Brazilian Center for Research in Physics (CBPF), respectively. His research interests are materials engineering and applied physics, with theoretical and experimental works on mechanical properties in metals, superconductors, high-pressure, interface metal-ceramic and X-ray diffraction.

Vinicius D. N. Bezzon, Universidade Federal de Ouro Preto

Vinicius D. N. Bezzon is currently Associate Professor in the Physics Department at the Federal University of Ouro Preto (UFOP). He had his M.Sc. and Ph.D. in Science and Technology of Materials at the State University of Sao Paulo (UNESP). His research interests are characterization the amorphous and crystalline materials using X-ray diffraction technique and characterization methods such as Rietveld refinement and pair distribution function analysis.

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Published

2024-08-31

How to Cite

Coelho Silva, M., Castro e Silva, A., T. D. Orlando, M., & D. N. Bezzon, V. (2024). Towards a Machine-Learning-Based Application for Amorphous Drug Recognition. IEEE Latin America Transactions, 22(9), 755–760. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/8988

Issue

Vol. 22 No. 9 (2024): Ordinary Issue

Section

Computing

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