【AI Explained】De novo molecular design and generative models.

Literature Information

TL;DR

This paper reviews the evolution of de novo molecular design strategies in drug discovery, highlighting advancements facilitated by machine learning and artificial intelligence over the past thirty years. It categorizes these approaches based on molecular representation methods and emphasizes the importance of robust benchmarks while addressing current challenges and future opportunities in the field.

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Artificial intelligence · Atom-based · Automated design · De novo design · Fragment-based

Abstract

Molecular design strategies are integral to therapeutic progress in drug discovery. Computational approaches for de novo molecular design have been developed over the past three decades and, recently, thanks in part to advances in machine learning (ML) and artificial intelligence (AI), the drug discovery field has gained practical experience. Here, we review these learnings and present de novo approaches according to the coarseness of their molecular representation: that is, whether molecular design is modeled on an atom-based, fragment-based, or reaction-based paradigm. Furthermore, we emphasize the value of strong benchmarks, describe the main challenges to using these methods in practice, and provide a viewpoint on further opportunities for exploration and challenges to be tackled in the upcoming years.

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Primary Questions Addressed

1. What are the key differences between atom-based, fragment-based, and reaction-based paradigms in de novo molecular design?
2. How have advancements in machine learning and artificial intelligence specifically impacted the efficiency of de novo molecular design?
3. What are the main challenges faced when implementing generative models in practical drug discovery processes?
4. Can you provide examples of successful applications of de novo molecular design in recent therapeutic developments?
5. What future opportunities and challenges do you foresee in the integration of computational approaches for molecular design?

Key Findings

Key Insights

1. Research Background and Objective: The field of drug discovery has been significantly influenced by molecular design strategies that facilitate the identification and development of therapeutic agents. Over the past thirty years, computational approaches for de novo molecular design have evolved, particularly with the integration of machine learning (ML) and artificial intelligence (AI). The objective of this study is to review the advancements in de novo molecular design, categorize these methodologies based on the granularity of molecular representation, and identify practical challenges and future opportunities in the application of these approaches in drug discovery.

2. Main Methods and Findings: The authors categorize de novo molecular design methodologies into three primary paradigms: atom-based, fragment-based, and reaction-based approaches. Each paradigm offers unique advantages and challenges for molecular representation and design. The review highlights the importance of establishing robust benchmarks to evaluate the efficacy of these computational methods in real-world applications. Additionally, the study outlines significant challenges in implementing these strategies, such as the balance between exploration and exploitation in molecular space, the need for high-quality data, and the integration of human expertise with automated systems. The authors also report practical experiences gained through applying these advanced methodologies in drug discovery, emphasizing their potential to enhance the efficiency and effectiveness of the design process.

3. Core Conclusions: The review concludes that de novo molecular design is poised to transform drug discovery, particularly through the use of AI and ML. However, the successful application of these technologies requires overcoming several hurdles, including the establishment of reliable benchmarks, addressing data quality issues, and improving the interpretability of generated molecular structures. The authors advocate for a continued focus on refining these computational strategies to ensure their practical utility in therapeutic development.

4. Research Significance and Impact: This work underscores the critical role of computational methodologies in accelerating drug discovery processes. By providing a comprehensive overview of current de novo molecular design strategies and the challenges they face, the study serves as a valuable resource for researchers and practitioners in the field. The insights presented can guide future research directions and foster collaborations between computational scientists and medicinal chemists. Ultimately, advancing de novo molecular design has the potential to lead to more efficient therapeutic development, thereby improving healthcare outcomes and addressing unmet medical needs.

Literatures Citing This Work

1. A Novel Scalarized Scaffold Hopping Algorithm with Graph-Based Variational Autoencoder for Discovery of JAK1 Inhibitors. - Yang Yu;Tingyang Xu;Jiawen Li;Yaping Qiu;Yu Rong;Zhen Gong;Xuemin Cheng;Liming Dong;Wei Liu;Jin Li;Dengfeng Dou;Junzhou Huang - ACS omega (2021)
2. RENATE: A Pseudo-retrosynthetic Tool for Synthetically Accessible de novo Design. - Gian Marco Ghiandoni;Michael J Bodkin;Beining Chen;Dimitar Hristozov;James E A Wallace;James Webster;Valerie J Gillet - Molecular informatics (2022)
3. Computational anti-COVID-19 drug design: progress and challenges. - Jinxian Wang;Ying Zhang;Wenjuan Nie;Yi Luo;Lei Deng - Briefings in bioinformatics (2022)
4. Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products. - Ana L Chávez-Hernández;K Eurídice Juárez-Mercado;Fernanda I Saldívar-González;José L Medina-Franco - Biomolecules (2021)
5. On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods. - Giovanni Bolcato;Esther Heid;Jonas Boström - Journal of chemical information and modeling (2022)
6. Natural product drug discovery in the artificial intelligence era. - F I Saldívar-González;V D Aldas-Bulos;J L Medina-Franco;F Plisson - Chemical science (2022)
7. Artificial Intelligence Technologies for COVID-19 De Novo Drug Design. - Giuseppe Floresta;Chiara Zagni;Davide Gentile;Vincenzo Patamia;Antonio Rescifina - International journal of molecular sciences (2022)
8. Software Assisted Multi-Tiered Mass Spectrometry Identification of Compounds in Traditional Chinese Medicine: Dalbergia odorifera as an Example. - Mengyuan Wang;Changliang Yao;Jiayuan Li;Xuemei Wei;Meng Xu;Yong Huang;Quanxi Mei;De-An Guo - Molecules (Basel, Switzerland) (2022)
9. Defining Levels of Automated Chemical Design. - Brian Goldman;Steven Kearnes;Trevor Kramer;Patrick Riley;W Patrick Walters - Journal of medicinal chemistry (2022)
10. Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets. - Vishwesh Venkatraman;Thomas H Colligan;George T Lesica;Daniel R Olson;Jeremiah Gaiser;Conner J Copeland;Travis J Wheeler;Amitava Roy - Frontiers in pharmacology (2022)

… (57 more literatures)

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