Deep Machine Learning for Computer-Aided Drug Design

Bajorath, Jürgen

doi:10.3389/fddsv.2022.829043

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…Otro ejemplo notable de los modelos generativos se observa en la creación de obras literarias, en donde se han generado desde poemas hasta libros de campos expertos del área de investigación (Petsko, 2019). En el diseño de fármacos, los modelos generativos se utilizan principalmente para diseñar nuevos compuestos químicos (Bajorath, 2022). La figura 4 resume algunas arquitecturas de DL empleadas en el diseño de novo, que son: Autocodificadores Variacionales (VAE, del inglés, Variational Autoencoders) (4a), Redes Generativas Antagónicas (GAN, Generative Adversarial Networks) (4b) y Redes Neuronales Recurrentes (RNN, Recurrent Neural Network) (4c).…”

Section: Diseño De Novo De Nuevos Compuestosunclassified

Inteligencia artificial en el diseño de fármacos: hacia la inteligencia aumentada

2023

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El desarrollo e investigación de fármacos es un proceso complejo que a menudo se percibe más como un arte que como una ciencia. Las muchas incógnitas de las enfermedades humanas, su complejidad, y el gran tamaño del espacio químico para encontrar fármacos potenciales, hacen que la toma de decisiones en Química Farmacéutica sea excepcionalmente exigente. Por tal motivo, durante décadas y especialmente en años recientes, se han integrado al proceso de diseño de fármacos modelos computacionales, y en particular, modelos de inteligencia artificial (IA) en combinación con su uso racional (inteligencia e intuición humana basada en la experiencia). Aquí se proporciona una descripción general y crítica de ejemplos recientes de aplicaciones de IA en el diseño y desarrollo de fármacos. Se hace una discusión de los conceptos y enfoques computacionales involucrados, y se analizan las posibilidades y limitaciones del diseño de fármacos utilizando IA en conjunto con su uso racional (o “el razonamiento humano”): inteligencia aumentada.

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Section: Diseño De Novo De Nuevos Compuestosunclassified

Inteligencia artificial en el diseño de fármacos: hacia la inteligencia aumentada

2023

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“…SBDD has reached notable maturity over the past decades, especially structure-based virtual screening, despite its intrinsic limitations. [10] In recent years, DL has been used in attempts to improve the performance of SBDD methods further. Perhaps the most well-known example of this is the usage of DL for protein structure prediction.…”

Section: Opportunities In Structure-based Drug Designmentioning

confidence: 99%

“…[8] Other notable applications of DL are predictions of chemical reactions [9], synthesis automation and de novo design. [10] The goal of the review is to discuss recent progress on selected concepts, resources, methodologies, and applications of CADD. Because of the broad scope of CADD, this manuscript is not meant to be a comprehensive review of the subject.…”

Section: Introductionmentioning

confidence: 99%

ViSAS for Entering Chemical Space: Virtual Screening of Analog Series and Related Advances

Naveja-Romero

Saldívar-González

Prado-Romero

et al. 2022

Preprint

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The manuscript discusses recent advances on computer-aided drug discovery (CADD) with focus on data-dependent drug discovery. Herein, we do not intend to review the many CADD methodologies comprehensively. Instead, the review discusses progress on selected concepts, methodologies, resources, and applications that are part of multidisciplinary efforts: the manuscript covers advances in artificial intelligence, machine learning, virtual screening, and chemical space including the concept of chemical multiverses, and novel extended similarity methods for chemical space exploration. Throughout the review, we emphasize public resources and open-source code available to the scientific community working in academia and non-profit institutions.

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“…Among them, the structure-based approach, also known as the structure-based drug design (SBDD), offers several advantages, including rapid target identification/validation/lead identification, and efficient lead optimization, by focusing on drug-binding sites ( Anderson, 2003 ; Kalyaanamoorthy and Chen, 2011 ). Moreover, by integrating computer-aided drug design techniques such as molecular docking-based virtual screening ( Maia et al, 2020 ), molecular dynamics simulations ( De Vivo et al, 2016 ), and machine learning ( Bajorath, 2022 ), the SBDD workflow can be further accelerated ( Sabe et al, 2021 ). Including computational tools, the specific processes and techniques requested in each step of the SBDD workflow are also described in Figure 1 .…”

Section: Introductionmentioning

confidence: 99%

Cryo-electron microscopy-based drug design

Cebi,

Lee,

Subramani

et al. 2024

Front. Mol. Biosci.

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Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

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Deep Machine Learning for Computer-Aided Drug Design

Cited by 16 publications

References 40 publications

Inteligencia artificial en el diseño de fármacos: hacia la inteligencia aumentada

Inteligencia artificial en el diseño de fármacos: hacia la inteligencia aumentada

ViSAS for Entering Chemical Space: Virtual Screening of Analog Series and Related Advances

Cryo-electron microscopy-based drug design

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