PILOT: equivariant diffusion for pocket-conditioned <i>de novo</i> ligand generation with multi-objective guidance <i>via</i> importance sampling

Cremer, Julian; Le, Tuan; Noé, Frank; Clevert, Djork-Arné; Schütt, Kristof T.

doi:10.1039/d4sc03523b

Chem. Sci.

2024

DOI: 10.1039/d4sc03523b

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PILOT: equivariant diffusion for pocket-conditioned de novo ligand generation with multi-objective guidance via importance sampling

Julian Cremer,

Tuan Le,

Frank Noé

et al.

Abstract: Creating ligands that fit specific protein pockets and possess desired chemical properties is a key challenge in SBDD. Guided 3D diffusion models present a promising solution, offering precise ligand generation with tailored properties.

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Diffusion Models in De Novo Drug Design

Alakhdar,

Poczos,

Washburn

2024

J. Chem. Inf. Model.

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Diffusion models have emerged as powerful tools for molecular generation, particularly in the context of 3D molecular structures. Inspired by nonequilibrium statistical physics, these models can generate 3D molecular structures with specific properties or requirements crucial to drug discovery. Diffusion models were particularly successful at learning the complex probability distributions of 3D molecular geometries and their corresponding chemical and physical properties through forward and reverse diffusion processes. This review focuses on the technical implementation of diffusion models tailored for 3D molecular generation. It compares the performance, evaluation methods, and implementation details of various diffusion models used for molecular generation tasks. We cover strategies for atom and bond representation, architectures of reverse diffusion denoising networks, and challenges associated with generating stable 3D molecular structures. This review also explores the applications of diffusion models in de novo drug design and related areas of computational chemistry, such as structure-based drug design, including target-specific molecular generation, molecular docking, and molecular dynamics of protein−ligand complexes. We also cover conditional generation on physical properties, conformation generation, and fragment-based drug design. By summarizing the state-of-the-art diffusion models for 3D molecular generation, this review sheds light on their role in advancing drug discovery and their current limitations.

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Diffusion Models in De Novo Drug Design

Alakhdar,

Poczos,

Washburn

2024

J. Chem. Inf. Model.

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Recent Advancements in the Application of Artificial Intelligence in Drug Molecular Generation and Synthesis Planning

Ma,

Wang,

et al. 2024

Pharmaceutical Fronts

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The design and synthesis of drug molecules is a pivotal stage in drug development that traditionally requires significant investment in time and finances. However, the integration of artificial intelligence (AI) in drug design accelerates the identification of potential drug candidates, optimizes the drug development process, and contributes to more informed decision-making. The application of AI in molecular generation is changing the way researchers explore the chemical space and design novel compounds. It accelerates the process of drug discovery and materials science, enabling rapid exploration of the vast chemical landscapes for the identification of promising candidates for further experimental validation. The application of AI in predicting reaction products accelerates the synthesis planning process, contributes to the automation of synthetic chemistry tasks, and supports chemists in making informed decisions during drug discovery. This paper reviewed the recent advances in two interrelated areas: the application of AI in molecular generation and synthesis routes. It will provide insights into the innovative ways in which AI is transforming traditional approaches in drug development and predict its future progress in these key fields.

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PILOT: equivariant diffusion for pocket-conditioned de novo ligand generation with multi-objective guidance via importance sampling

Abstract: Creating ligands that fit specific protein pockets and possess desired chemical properties is a key challenge in SBDD. Guided 3D diffusion models present a promising solution, offering precise ligand generation with tailored properties.

Cited by 2 publications

References 23 publications

Diffusion Models in De Novo Drug Design

Diffusion Models in De Novo Drug Design

Recent Advancements in the Application of Artificial Intelligence in Drug Molecular Generation and Synthesis Planning

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