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DNA-encoded libraries (DELs) combined with machine learning (ML) offer a powerful paradigm for hit identification. However, sequencing-derived enrichment data are inherently noisy and biased, often resulting in models that overfit to specific chemical libraries. In this review, we critically evaluate the capabilities and limitations of DEL-ML, illustrating key challenges using Aurora Kinase A (AURKA) DEL affinity selection data. We demonstrate that standard ML models often struggle to generalize to unseen chemical space because of the specific structural constraints of combinatorial libraries. Furthermore, we discuss the necessity of rigorous denoising strategies and evaluate approaches, such as domain adaptation, to mitigate these limitations, offering a roadmap for building robust models capable of exploring diverse chemical space.

The process of developing new drugs is arduous and costly, particularly for targets classified as "difficult-to-drug." Macrocycles show a particular ability to modulate difficult-to-drug targets, including protein-protein interactions, while still allowing oral administration. However, the determination of membrane permeability, critical for reaching intracellular targets and for oral bioavailability, is laborious and expensive. In silico methods are a cost-effective alternative, enabling predictions prior to compound synthesis. Here, we present a comprehensive online database (https://swemacrocycledb.com/), housing 5638 membrane permeability datapoints for 4216 nonpeptidic macrocycles, curated from the literature, patents, and bioactivity repositories. In addition, we present a new descriptor, the "amide ratio" (AR), that quantifies the peptidic nature of macrocyclic compounds, enabling the classification of peptidic, semipeptidic, and nonpeptidic macrocycles. Overall, this resource fills a gap among existing databases, offering valuable insights into the membrane permeability of nonpeptidic and semipeptidic macrocycles, and facilitating predictions for drug discovery projects.

The ability to adopt folded conformations that have a low solvent-accessible 3D polar surface area has been found to be important for PROTACs to display a high passive cell permeability. We have studied two VHL PROTACs that differ only by the replacement of two methylene groups in the linker by oxygen atoms but that displayed vast differences in their cell permeability. MD simulations and NMR spectroscopy revealed an unexpected, environment-dependent conformational behavior for the low-permeability PROTAC that has an alkyl linker. Hydrophobic interactions enforced extended and polar conformations for this PROTAC in nonpolar media, explaining its low cell permeability. In water, hydrophobic collapse around the linker led to folded and less polar conformations. In contrast, the highly permeable PROTAC having a PEG linker adopted conformations of similar shapes and polarities in polar and nonpolar environments.

The discovery of the E3 ligase cereblon (CRBN) as the target of thalidomide and its analogs revolutionized the field of targeted protein degradation (TPD). This ubiquitin-mediated degradation pathway was first harnessed by bivalent degraders. Recently, the emergence of low-molecular-weight molecular glue degraders (MGDs) has expanded the TPD landscape, because MGDs operate via the same mechanism while offering attractive physicochemical properties that are consistent with small-molecule therapeutics. This review delves into the discovery and advancement of MGDs, with case studies on cyclin K and the zinc finger protein IKZF2, highlighting the design principles, biological assays and therapeutic applications. Additionally, it examines the chemical space of molecular glues and outlines the collaborative efforts that are fueling innovation in this field.

Orally bioavailable, high molecular weight macrocyclic peptides that inhibit difficult-to-drug protein-protein interactions are of high therapeutic value, and rules for their design were proposed recently. Here, we emphasize the danger of rules that provide a false impression of the lipophilicity required of a clinical candidate.

Computational Drug Discovery: Methods and Applications (2 volume set) covers a wide range of cutting-edge computational technologies and computational chemistry methods that are transforming drug discovery. The book delves into recent advances, particularly focusing on artificial intelligence (AI) and its application for protein structure prediction, AI-enabled virtual screening, and generative modeling for compound design. Additionally, it covers key technological advancements in computing such as quantum and cloud computing that are driving innovations in drug discovery.

Furthermore, dedicated chapters that addresses the recent trends in the field of computer aided drug design, including ultra-large-scale virtual screening for hit identification, computational strategies for designing new therapeutic modalities like PROTACs and covalent inhibitors that target residues beyond cysteine are also presented.

To offer the most up-to-date information on computational methods utilized in Computational Drug Discovery, it covers chapters highlighting the use of molecular dynamics and other related methods, application of QM and QM/MM methods in computational drug design, and techniques for navigating and visualizing the chemical space, as well as leveraging big data to drive drug discovery efforts.

The book is thoughtfully organized into eight thematic sections, each focusing on a specific computational method or technology applied to drug discovery. Authored by renowned experts from academia, pharmaceutical industry, and major drug discovery software providers, it offers an overview of the latest advances in computational drug discovery.

Key topics covered in the book include:

• Application of molecular dynamics simulations and related approaches in drug discovery
• The application of QM, hybrid approaches such as QM/MM, and fragment molecular orbital framework for understanding protein-ligand interactions
• Adoption of artificial intelligence in pre-clinical drug discovery, encompassing protein structure prediction, generative modeling for de novo design, and virtual screening.
• Techniques for navigating and visualizing the chemical space, along with harnessing big data to drive drug discovery efforts.
• Methods for performing ultra-large-scale virtual screening for hit identification.
• Computational strategies for designing new therapeutic models, including PROTACs and molecular glues.
• In silico ADMET approaches for predicting a variety of pharmacokinetic and physicochemical endpoints.
• The role of computing technologies like quantum computing and cloud computing in accelerating drug discovery

This book will provide readers an overview of the latest advancements in Computational Drug Discovery and serve as a valuable resource for professionals engaged in drug discovery.

Macrocycles are prominent among drugs for treatment of infectious disease, with many originating from natural products. Herein we report on the discovery of a series of macrocycles structurally related to the natural product hymenocardine. Members of this series were found to inhibit the growth of Plasmodium falciparum, the parasite responsible for most malaria cases, and of four kinetoplastid parasites. Notably, macrocycles more potent than miltefosine, the only oral drug used for the treatment of the neglected tropical disease visceral leishmaniasis, were identified in a phenotypic screen of Leishmania infantum. In vitro profiling highlighted that potent inhibitors had satisfactory cell permeability with a low efflux ratio, indicating their potential for oral administration, but low solubility and metabolic stability. Analysis of predicted crystal structures suggests that optimization should focus on the reduction of pi-pi crystal packing interactions to reduce the strong crystalline interactions and improve the solubility of the most potent lead.

A principal challenge in the discovery of proteolysis targeting chimeras (PROTACs) as oral medications is their bioavailability. To facilitate drug design, it is therefore essential to identify the chemical space where orally bioavailable PROTACs are more likely to be situated. To this aim, we extracted structure-bioavailability insights from published data using traditional 2D descriptors, thereby shedding light on their potential and limitations as drug design tools. Subsequently, we describe cuttingedge experimental, computational and hybrid design strategies based on 3D descriptors, which show promise for enhancing the probability of discovering PROTACs with high oral bioavailability.

Introduction: Hydrophobic tagging (HyT) technology presents a distinct therapeutic strategy diverging from conventional small molecule drugs, providing an innovative approach to drug design. This review aims to provide an overview of the HyT literature and future outlook to offer guidance for drug design.

Areas covered: In this review, the authors introduce the composition, mechanisms and advantages of HyT technology, as well as summarize the detailed applications of HyT technology in anti-cancer, neurodegenerative diseases (NDs), autoimmune disorders, cardiovascular diseases (CVDs), and other fields. Furthermore, this review discusses key aspects of the future development of HyT molecules.

Expert opinion: HyT emerges as a highly promising targeted protein degradation (TPD) strategy, following the successful development of proteolysis targeting chimeras (PROTAC) and molecular glue. Based on exploring new avenues, modification of the HyT molecule itself potentially enhances the technology. Improved synthetic pathways and emphasis on pharmacokinetic (PK) properties will facilitate the development of HyT. Furthermore, elucidating the biochemical basis by which the compound's hydrophobic moiety recruits the protein homeostasis network will enable the development of more precise assays that can guide the optimization of the linker and hydrophobic moiety.

The discovery of cell permeable and orally bioavailable von Hippel-Lindau (VHL) proteolysis targeting chimeras (PROTACs) is challenging as their structures locates them at, or beyond, the outer limits of oral druggable space. We have designed a set of nine VHL PROTACs and found that the linker had a profound impact on passive cell permeability. Determination of the solution ensembles in a nonpolar solvent revealed that high permeability was correlated to the ability of the PROTACs to adopt folded conformations that have a low solvent accessible 3D polar surface area. Our results suggest that the design of cell permeable VHL PROTACs could focus on linkers that facilitate shielding of polar surface area in the VHL ligand in a nonpolar but not in a polar environment. In addition, we found that not only intramolecular hydrogen bonds, but also NH-pi and pi-pi interactions contribute to the stabilization of low-polarity conformations, and thereby to high cell permeability.