Thompson Sampling─An Efficient Method for Searching Ultralarge Synthesis on Demand Databases.

Over the last five years, virtual screening of ultralarge synthesis on-demand libraries has emerged as a powerful tool for hit identification in drug discovery programs. As these libraries have grown to tens of billions of molecules, we have reached a point where it is no longer cost-effective to screen every molecule virtually. To address these challenges, several groups have developed heuristic search methods to rapidly identify the best molecules on a virtual screen. This article describes the application of Thompson sampling (TS), an active learning approach that streamlines the virtual screening of large combinatorial libraries by performing a probabilistic search in the reagent space, thereby never requiring the full enumeration of the library. TS is a general technique that can be applied to various virtual screening modalities, including 2D and 3D similarity search, docking, and application of machine-learning models. In an illustrative example, we show that TS can identify more than half of the top 100 molecules from a docking-based virtual screen of 335 million molecules by evaluating 1% of the data set.

Exploring the combinatorial explosion of amine-acid reaction space via graph editing.

Amines and carboxylic acids are abundant chemical feedstocks that are nearly exclusively united via the amide coupling reaction. The disproportionate use of the amide coupling leaves a large section of unexplored reaction space between amines and acids: two of the most common chemical building blocks. Herein we conduct a thorough exploration of amine-acid reaction space via systematic enumeration of reactions involving a simple amine-carboxylic acid pair. This approach to chemical space exploration investigates the coarse and fine modulation of physicochemical properties and molecular shapes. With the invention of reaction methods becoming increasingly automated and bringing conceptual reactions into reality, our map provides an entirely new axis of chemical space exploration for rational property design.

Discovery and Clinical Proof-of-Concept of RLY-2608, a First-in-Class Mutant-Selective Allosteric PI3Kα Inhibitor That Decouples Antitumor Activity from Hyperinsulinemia.

PIK3CA (PI3Kα) is a lipid kinase commonly mutated in cancer, including ∼40% of hormone receptor-positive breast cancer. The most frequently observed mutants occur in the kinase and helical domains. Orthosteric PI3Kα inhibitors suffer from poor selectivity leading to undesirable side effects, most prominently hyperglycemia due to inhibition of wild-type (WT) PI3Kα. Here, we used molecular dynamics simulations and cryo-electron microscopy to identify an allosteric network that provides an explanation for how mutations favor PI3Kα activation. A DNA-encoded library screen leveraging electron microscopy-optimized constructs, differential enrichment, and an orthosteric-blocking compound led to the identification of RLY-2608, a first-in-class allosteric mutant-selective inhibitor of PI3Kα. RLY-2608 inhibited tumor growth in PIK3CA-mutant xenograft models with minimal impact on insulin, a marker of dysregulated glucose homeostasis. RLY-2608 elicited objective tumor responses in two patients diagnosed with advanced hormone receptor-positive breast cancer with kinase or helical domain PIK3CA mutations, with no observed WT PI3Kα-related toxicities. SIGNIFICANCE: Treatments for PIK3CA-mutant cancers are limited by toxicities associated with the inhibition of WT PI3Kα. Molecular dynamics, cryo-electron microscopy, and DNA-encoded libraries were used to develop RLY-2608, a first-in-class inhibitor that demonstrates mutant selectivity in patients. This marks the advance of clinical mutant-selective inhibition that overcomes limitations of orthosteric PI3Kα inhibitors. See related commentary by Gong and Vanhaesebroeck, p. 204 . See related article by Varkaris et al., p. 227 . This article is featured in Selected Articles from This Issue, p. 201.

Assessment of protein-ligand complexes in CASP15.

CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.

Identification of GDC-1971 (RLY-1971), a SHP2 Inhibitor Designed for the Treatment of Solid Tumors.

Protein tyrosine phosphatase SHP2 mediates RAS-driven MAPK signaling and has emerged in recent years as a target of interest in oncology, both for treating with a single agent and in combination with a KRAS inhibitor. We were drawn to the pharmacological potential of SHP2 inhibition, especially following the initial observation that drug-like compounds could bind an allosteric site and enforce a closed, inactive state of the enzyme. Here, we describe the identification and characterization of GDC-1971 (formerly RLY-1971), a SHP2 inhibitor currently in clinical trials in combination with KRAS G12C inhibitor divarasib (GDC-6036) for the treatment of solid tumors driven by a KRAS G12C mutation.

Epilogue to the Gerald Maggiora Festschrift: a tribute to an exemplary mentor, colleague, collaborator, and innovator.

In May 2022, JCAMD published a Special Issue in honor of Gerald (Gerry) Maggiora, whose scientific leadership over many decades advanced the fields of computational chemistry and chemoinformatics for drug discovery. Along the way, he has impacted many researchers in both academia and the pharmaceutical industry. In this Epilogue, we explain the origins of the Festschrift and present a series of first-hand vignettes, in approximate chronological sequence, that together paint a picture of this remarkable man. Whether they highlight Gerry's endless curiosity about molecular life sciences or his willingness to challenge conventional wisdom or his generous support of junior colleagues and peers, these colleagues and collaborators are united in their appreciation of his positive influence. These tributes also reflect key trends and themes during the evolution of modern drug discovery, seen through the lens of people who worked with a visionary leader. Junior scientists will find an inspiring roadmap for creative collegiality and collaboration.

Defining Levels of Automated Chemical Design.

One application area of computational methods in drug discovery is the automated design of small molecules. Despite the large number of publications describing methods and their application in both retrospective and prospective studies, there is a lack of agreement on terminology and key attributes to distinguish these various systems. We introduce Automated Chemical Design (ACD) Levels to clearly define the level of autonomy along the axes of ideation and decision making. To fully illustrate this framework, we provide literature exemplars and place some notable methods and applications into the levels. The ACD framework provides a common language for describing automated small molecule design systems and enables medicinal chemists to better understand and evaluate such systems.

Critical assessment of AI in drug discovery.

Introduction: Artificial Intelligence (AI) has become a component of our everyday lives, with applications ranging from recommendations on what to buy to the analysis of radiology images. Many of the techniques originally developed for other fields such as language translation and computer vision are now being applied in drug discovery. AI has enabled multiple aspects of drug discovery including the analysis of high content screening data, and the design and synthesis of new molecules.Areas covered: This perspective provides an overview of the application of AI in several areas relevant to drug discovery including property prediction, molecule generation, image analysis, and organic synthesis planning.Expert opinion: While a variety of machine learning methods are now being routinely used to predict biological activity and ADME properties, methods of representing molecules continue to evolve. Molecule generation methods are relatively new and unproven but hold the potential to access new, unexplored areas of chemical space. The application of AI in drug discovery will continue to benefit from dedicated research, as well as AI developments in other fields. With this pairing algorithmic advancements and high-quality data, the impact of AI in drug discovery will continue to grow in the coming years.

Applications of Deep Learning in Molecule Generation and Molecular Property Prediction.

Recent advances in computer hardware and software have led to a revolution in deep neural networks that has impacted fields ranging from language translation to computer vision. Deep learning has also impacted a number of areas in drug discovery, including the analysis of cellular images and the design of novel routes for the synthesis of organic molecules. While work in these areas has been impactful, a complete review of the applications of deep learning in drug discovery would be beyond the scope of a single Account. In this Account, we will focus on two key areas where deep learning has impacted molecular design: the prediction of molecular properties and the de novo generation of suggestions for new molecules.One of the most significant advances in the development of quantitative structure-activity relationships (QSARs) has come from the application of deep learning methods to the prediction of the biological activity and physical properties of molecules in drug discovery programs. Rather than employing the expert-derived chemical features typically used to build predictive models, researchers are now using deep learning to develop novel molecular representations. These representations, coupled with the ability of deep neural networks to uncover complex, nonlinear relationships, have led to state-of-the-art performance. While deep learning has changed the way that many researchers approach QSARs, it is not a panacea. As with any other machine learning task, the design of predictive models is dependent on the quality, quantity, and relevance of available data. Seemingly fundamental issues, such as optimal methods for creating a training set, are still open questions for the field. Another critical area that is still the subject of multiple research efforts is the development of methods for assessing the confidence in a model.Deep learning has also contributed to a renaissance in the application of de novo molecule generation. Rather than relying on manually defined heuristics, deep learning methods learn to generate new molecules based on sets of existing molecules. Techniques that were originally developed for areas such as image generation and language translation have been adapted to the generation of molecules. These deep learning methods have been coupled with the predictive models described above and are being used to generate new molecules with specific predicted biological activity profiles. While these generative algorithms appear promising, there have been only a few reports on the synthesis and testing of molecules based on designs proposed by generative models. The evaluation of the diversity, quality, and ultimate value of molecules produced by generative models is still an open question. While the field has produced a number of benchmarks, it has yet to agree on how one should ultimately assess molecules "invented" by an algorithm.

Code Sharing in the Open Science Era.

Many high-profile scientific journals have established policies mandating the release of code accompanying papers that describe computational methods. Unfortunately, the majority of journals that publish papers in Computational Chemistry and Cheminformatics have yet to define such guidelines. This Viewpoint reviews the current state of reproducibility for the field and makes a case for the inclusion of code with computational papers.

D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.

Rethinking drug design in the artificial intelligence era.

Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.

D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings.

The Drug Design Data Resource aims to test and advance the state of the art in protein-ligand modeling by holding community-wide blinded, prediction challenges. Here, we report on our third major round, Grand Challenge 3 (GC3). Held 2017-2018, GC3 centered on the protein Cathepsin S and the kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1, and included both pose-prediction and affinity-ranking components. GC3 was structured much like the prior challenges GC2015 and GC2. First, Stage 1 tested pose prediction and affinity ranking methods; then all available crystal structures were released, and Stage 2 tested only affinity rankings, now in the context of the available structures. Unique to GC3 was the addition of a Stage 1b self-docking subchallenge, in which the protein coordinates from all of the cocrystal structures used in the cross-docking challenge were released, and participants were asked to predict the pose of CatS ligands using these newly released structures. We provide an overview of the outcomes and discuss insights into trends and best-practices.

Virtual Chemical Libraries.

Advances in computer processing speed and storage capacity have enabled researchers to generate virtual chemical libraries containing billions of molecules. While these numbers appear large, they are only a small fraction of the number of organic molecules that could potentially be synthesized. This review provides an overview of recent advances in the generation and use of virtual chemical libraries in medicinal chemistry. We also consider the practical implications of these libraries in drug discovery programs and highlight a number of current and future challenges.