Agents HCLS Applications

HCLS Application

Background Readings:

Structure-based drug design with geometric deep learning

• https://doi.org/10.1016/j.sbi.2023.102548

• Structure-based drug design uses three-dimensional geometric information of macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric deep learning, an emerging concept of neural-network-based machine learning, has been applied to macromolecular structures. This review provides an overview of the recent applications of geometric deep learning in bioorganic and medicinal chemistry, highlighting its potential for structure-based drug discovery and design. Emphasis is placed on molecular property prediction, ligand binding site and pose prediction, and structure-based de novo molecular design. The current challenges and opportunities are highlighted, and a forecast of the future of geometric deep learning for drug discovery is presented. Questions answered in this article

• Structure-based drug design is based on methods that leverage three-dimensional (3D) structures of macromolecular targets, such as proteins and nucleic acids, for decision-making in medicinal chemistry [1,2]. Structure-based modeling is well established throughout the drug discovery process, aiming to rationalize non-covalent interactions between ligands and their target macromolecule(s) [3]. The questions addressed with structure-based approaches include molecular property prediction, ligand binding site recognition, binding pose estimation, as well as de novo design [4, 5, 6, 7]. For such tasks, detailed knowledge of the 3D structure of the investigated macromolecular surfaces and ligand–receptor interfaces is essential. Recently, an emerging concept of neural-network-based “artificial intelligence”, geometric deep learning, has been introduced to solve numerous problems in the molecular sciences, including structure-based drug discovery and design [8].

Generative models for molecular discovery: Recent advances and challenges

• Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, Klavs F. Jensen
• 05 March 2022 https://doi.org/10.1002/wcms.1608Citations
• Development of new products often relies on the discovery of novel molecules. While conventional molecular design involves using human expertise to propose, synthesize, and test new molecules, this process can be cost and time intensive, limiting the number of molecules that can be reasonably tested. Generative modeling provides an alternative approach to molecular discovery by reformulating molecular design as an inverse design problem. Here, we review the recent advances in the state-of-the-art of generative molecular design and discusses the considerations for integrating these models into real molecular discovery campaigns. We first review the model design choices required to develop and train a generative model including common 1D, 2D, and 3D representations of molecules and typical generative modeling neural network architectures. We then describe different problem statements for molecular discovery applications and explore the benchmarks used to evaluate models based on those problem statements. Finally, we discuss the important factors that play a role in integrating generative models into experimental workflows. Our aim is that this review will equip the reader with the information and context necessary to utilize generative modeling within their domain.

More Readings:

The rise of agentic AI teammates in medicine

• Perspectives Digital medicine Volume 405, Issue 10477 vp457 February 08, 2025
• James Zou jamesz@stanford.edu ∙ Eric J Topol
• Medicine is in the dawn of a fundamental shift from using artificial intelligence (AI) as tools to deploying AI as agents. When used as a tool, AI is passive and reactive. Even powerful medical AI foundation models today remain tools that depend on human users to provide input and context, interpret their output, and take follow-up steps. To fully unlock AI’s potential in medicine, clinicians need to make the key conceptual shift from using AI as sophisticated calculators to embracing AI as health-care teammates.

Lab-in-the-loop therapeutic antibody design with deep learning

• https://doi.org/10.1101/2025.02.19.639050
• Therapeutic antibody design is a complex multi-property optimization problem that traditionally relies on expensive search through sequence space. Here, we introduce “Lab-in-the-loop,” a paradigm shift for antibody design that orchestrates generative machine learning models, multi-task property predictors, active learning ranking and selection, and in vitro experimentation in a semiautonomous, iterative optimization loop. By automating the design of antibody variants, property prediction, ranking and selection of designs to assay in the lab, and ingestion of in vitro data, we enable a holistic, end-to-end approach to antibody optimization. We apply lab-in-the-loop to four clinically relevant antigen targets: EGFR, IL-6, HER2, and OSM. Over 1,800 unique antibody variants are designed and tested, derived from lead molecule candidates obtained via animal immunization and state-of-the-art immune repertoire mining techniques. Four lead candidate and four design crystal structures are solved to reveal mechanistic insights into the effects of mutations. We perform four rounds of iterative optimization and report 3–100× better binding variants for every target and ten candidate lead molecules, with the best binders in a therapeutically relevant 100 pM range.
• All authors are or were employees of Genentech Inc. (a member of the Roche Group) or Roche, and may hold Roche stock or related interests.