Prospective de novo drug design with deep interactome learning.

De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.

Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.

Enabling late-stage drug diversification by high-throughput experimentation with geometric deep learning.

Late-stage functionalization is an economical approach to optimize the properties of drug candidates. However, the chemical complexity of drug molecules often makes late-stage diversification challenging. To address this problem, a late-stage functionalization platform based on geometric deep learning and high-throughput reaction screening was developed. Considering borylation as a critical step in late-stage functionalization, the computational model predicted reaction yields for diverse reaction conditions with a mean absolute error margin of 4-5%, while the reactivity of novel reactions with known and unknown substrates was classified with a balanced accuracy of 92% and 67%, respectively. The regioselectivity of the major products was accurately captured with a classifier F-score of 67%. When applied to 23 diverse commercial drug molecules, the platform successfully identified numerous opportunities for structural diversification. The influence of steric and electronic information on model performance was quantified, and a comprehensive simple user-friendly reaction format was introduced that proved to be a key enabler for seamlessly integrating deep learning and high-throughput experimentation for late-stage functionalization.

Identifying opportunities for late-stage C-H alkylation with high-throughput experimentation and in silico reaction screening.

Enhancing the properties of advanced drug candidates is aided by the direct incorporation of specific chemical groups, avoiding the need to construct the entire compound from the ground up. Nevertheless, their chemical intricacy often poses challenges in predicting reactivity for C-H activation reactions and planning their synthesis. We adopted a reaction screening approach that combines high-throughput experimentation (HTE) at a nanomolar scale with computational graph neural networks (GNNs). This approach aims to identify suitable substrates for late-stage C-H alkylation using Minisci-type chemistry. GNNs were trained using experimentally generated reactions derived from in-house HTE and literature data. These trained models were then used to predict, in a forward-looking manner, the coupling of 3180 advanced heterocyclic building blocks with a diverse set of sp3-rich carboxylic acids. This predictive approach aimed to explore the substrate landscape for Minisci-type alkylations. Promising candidates were chosen, their production was scaled up, and they were subsequently isolated and characterized. This process led to the creation of 30 novel, functionally modified molecules that hold potential for further refinement. These results positively advocate the application of HTE-based machine learning to virtual reaction screening.

Allosteric targeting resolves limitations of earlier LFA-1 directed modalities.

Integrins are a family of cell surface receptors well-recognized for their therapeutic potential in a wide range of diseases. However, the development of integrin targeting medications has been impacted by unexpected downstream effects, reflecting originally unforeseen interference with the bidirectional signalling and cross-communication of integrins. We here selected one of the most severely affected target integrins, the integrin lymphocyte function-associated antigen-1 (LFA-1, αLß2, CD11a/CD18), as a prototypic integrin to systematically assess and overcome these known shortcomings. We employed a two-tiered ligand-based virtual screening approach to identify a novel class of allosteric small molecule inhibitors targeting this integrin's αI domain. The newly discovered chemical scaffold was derivatized, yielding potent bis-and tris-aryl-bicyclic-succinimides which inhibit LFA-1 in vitro at low nanomolar concentrations. The characterisation of these compounds in comparison to earlier LFA-1 targeting modalities established that the allosteric LFA-1 inhibitors (i) are devoid of partial agonism, (ii) selectively bind LFA-1 versus other integrins, (iii) do not trigger internalization of LFA-1 itself or other integrins and (iv) display oral availability. This profile differentiates the new generation of allosteric LFA-1 inhibitors from previous ligand mimetic-based LFA-1 inhibitors and anti-LFA-1 antibodies, and is projected to support novel immune regulatory regimens selectively targeting the integrin LFA-1. The rigorous computational and experimental assessment schedule described here is designed to be adaptable to the preclinical discovery and development of novel allosterically acting compounds targeting integrins other than LFA-1, providing an exemplary approach for the early characterisation of next generation integrin inhibitors.

Structure-based drug design with geometric deep learning.

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.

Leveraging molecular structure and bioactivity with chemical language models for de novo drug design.

Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM. This virtual compound library was refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ ligands. Several of the computer-generated molecular designs were commercially available, enabling fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified, highlighting the method's scaffold-hopping potential. Chemical synthesis and biochemical testing of two of the top-ranked de novo designed molecules and their derivatives corroborated the model's ability to generate PI3Kγ ligands with medium to low nanomolar activity for hit-to-lead expansion. The most potent compounds led to pronounced inhibition of PI3K-dependent Akt phosphorylation in a medulloblastoma cell model, demonstrating efficacy of PI3Kγ ligands in PI3K/Akt pathway repression in human tumor cells. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design.

Machine Learning and Computational Chemistry for the Endocannabinoid System.

Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.

Detection of cannabinoid receptor type 2 in native cells and zebrafish with a highly potent, cell-permeable fluorescent probe.

Despite its essential role in the (patho)physiology of several diseases, CB2R tissue expression profiles and signaling mechanisms are not yet fully understood. We report the development of a highly potent, fluorescent CB2R agonist probe employing structure-based reverse design. It commences with a highly potent, preclinically validated ligand, which is conjugated to a silicon-rhodamine fluorophore, enabling cell permeability. The probe is the first to preserve interspecies affinity and selectivity for both mouse and human CB2R. Extensive cross-validation (FACS, TR-FRET and confocal microscopy) set the stage for CB2R detection in endogenously expressing living cells along with zebrafish larvae. Together, these findings will benefit clinical translatability of CB2R based drugs.

QMugs, quantum mechanical properties of drug-like molecules.

Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there currently is a lack of data collections featuring large bioactive molecules alongside first-principle quantum chemical information. The open-access QMugs (Quantum-Mechanical Properties of Drug-like Molecules) dataset fills this void. The QMugs collection comprises quantum mechanical properties of more than 665 k biologically and pharmacologically relevant molecules extracted from the ChEMBL database, totaling ~2 M conformers. QMugs contains optimized molecular geometries and thermodynamic data obtained via the semi-empirical method GFN2-xTB. Atomic and molecular properties are provided on both the GFN2-xTB and on the density-functional levels of theory (DFT, ωB97X-D/def2-SVP). QMugs features molecules of significantly larger size than previously-reported collections and comprises their respective quantum mechanical wave functions, including DFT density and orbital matrices. This dataset is intended to facilitate the development of models that learn from molecular data on different levels of theory while also providing insight into the corresponding relationships between molecular structure and biological activity.

Translating from Proteins to Ribonucleic Acids for Ligand-binding Site Detection.

Identifying druggable ligand-binding sites on the surface of the macromolecular targets is an important process in structure-based drug discovery. Deep-learning models have been shown to successfully predict ligand-binding sites of proteins. As a step toward predicting binding sites in RNA and RNA-protein complexes, we employ three-dimensional convolutional neural networks. We introduce a dataset splitting approach to minimize structure-related bias in training data, and investigate the influence of protein-based neural network pre-training before fine-tuning on RNA structures. Models that were pre-trained on proteins considerably outperformed the models that were trained exclusively on RNA structures. Overall, 71 % of the known RNA binding sites were correctly located within 4 Šof their true centres.

Δ-Quantum machine-learning for medicinal chemistry.

Many molecular design tasks benefit from fast and accurate calculations of quantum-mechanical (QM) properties. However, the computational cost of QM methods applied to drug-like molecules currently renders large-scale applications of quantum chemistry challenging. Aiming to mitigate this problem, we developed DelFTa, an open-source toolbox for the prediction of electronic properties of drug-like molecules at the density functional (DFT) level of theory, using Δ-machine-learning. Δ-Learning corrects the prediction error (Δ) of a fast but inaccurate property calculation. DelFTa employs state-of-the-art three-dimensional message-passing neural networks trained on a large dataset of QM properties. It provides access to a wide array of quantum observables on the molecular, atomic and bond levels by predicting approximations to DFT values from a low-cost semiempirical baseline. Δ-Learning outperformed its direct-learning counterpart for most of the considered QM endpoints. The results suggest that predictions for non-covalent intra- and intermolecular interactions can be extrapolated to larger biomolecular systems. The software is fully open-sourced and features documented command-line and Python APIs.

Cannabinoid receptor type 2 ligands: an analysis of granted patents since 2010.

The G-protein-coupled cannabinoid receptor type 2 (CB2R) is a key element of the endocannabinoid (EC) system. EC/CB2R signaling has significant therapeutic potential in major pathologies affecting humans such as allergies, neurodegenerative disorders, inflammation or ocular diseases. CB2R agonism exerts anti-inflammatory and tissue protective effects in preclinical animal models of cardiovascular, gastrointestinal, liver, kidney, lung and neurodegenerative disorders. Existing ligands can be subdivided into endocannabinoids, cannabinoid-like and synthetic CB2R ligands that possess various degrees of potency on and selectivity against the cannabinoid receptor type 1. This review is an account of granted CB2R ligand patents from 2010 up to the present, which were surveyed using Derwent Innovation®.

Combining generative artificial intelligence and on-chip synthesis for de novo drug design.

Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding for drug discovery. Using deep learning for molecular design and a microfluidics platform for on-chip chemical synthesis, liver X receptor (LXR) agonists were generated from scratch. The computational pipeline was tuned to explore the chemical space of known LXRα agonists and generate novel molecular candidates. To ensure compatibility with automated on-chip synthesis, the chemical space was confined to the virtual products obtainable from 17 one-step reactions. Twenty-five de novo designs were successfully synthesized in flow. In vitro screening of the crude reaction products revealed 17 (68%) hits, with up to 60-fold LXR activation. The batch resynthesis, purification, and retesting of 14 of these compounds confirmed that 12 of them were potent LXR agonists. These results support the suitability of the proposed design-make-test-analyze framework as a blueprint for automated drug design with artificial intelligence and miniaturized bench-top synthesis.

Development of High-Specificity Fluorescent Probes to Enable Cannabinoid Type 2 Receptor Studies in Living Cells.

Pharmacological modulation of cannabinoid type 2 receptor (CB2R) holds promise for the treatment of numerous conditions, including inflammatory diseases, autoimmune disorders, pain, and cancer. Despite the significance of this receptor, researchers lack reliable tools to address questions concerning the expression and complex mechanism of CB2R signaling, especially in cell-type and tissue-dependent contexts. Herein, we report for the first time a versatile ligand platform for the modular design of a collection of highly specific CB2R fluorescent probes, used successfully across applications, species, and cell types. These include flow cytometry of endogenously expressing cells, real-time confocal microscopy of mouse splenocytes and human macrophages, as well as FRET-based kinetic and equilibrium binding assays. High CB2R specificity was demonstrated by competition experiments in living cells expressing CB2R at native levels. The probes were effectively applied to FACS analysis of microglial cells derived from a mouse model relevant to Alzheimer's disease.

Identification and Preclinical Development of a 2,5,6-Trisubstituted Fluorinated Pyridine Derivative as a Radioligand for the Positron Emission Tomography Imaging of Cannabinoid Type 2 Receptors.

Despite the broad implications of the cannabinoid type 2 receptor (CB2) in neuroinflammatory processes, a suitable CB2-targeted probe is currently lacking in clinical routine. In this work, we synthesized 15 fluorinated pyridine derivatives and tested their binding affinities toward CB2 and CB1. With a sub-nanomolar affinity (Ki for CB2) of 0.8 nM and a remarkable selectivity factor of >12,000 over CB1, RoSMA-18-d6 exhibited outstanding in vitro performance characteristics and was radiofluorinated with an average radiochemical yield of 10.6 ± 3.8% (n = 16) and molar activities ranging from 52 to 65 GBq/µmol (radiochemical purity > 99%). [18F]RoSMA-18-d6 showed exceptional CB2 attributes as demonstrated by in vitro autoradiography, ex vivo biodistribution, and positron emission tomography (PET). Further, [18F]RoSMA-18-d6 was used to detect CB2 upregulation on postmortem human ALS spinal cord tissues. Overall, these results suggest that [18F]RoSMA-18-d6 is a promising CB2 PET radioligand for clinical translation.

NMR pseudocontact shifts in a symmetric protein homotrimer.

NMR pseudocontact shifts are a valuable tool for structural and functional studies of proteins. Protein multimers mediate key functional roles in biology, but methods for their study by pseudocontact shifts are so far not available. Paramagnetic tags attached to identical subunits in multimeric proteins cause a combined pseudocontact shift that cannot be described by the standard single-point model. Here, we report pseudocontact shifts generated simultaneously by three paramagnetic Tm-M7PyThiazole-DOTA tags to the trimeric molecular chaperone Skp and provide an approach for the analysis of this and related symmetric systems. The pseudocontact shifts were described by a "three-point" model, in which positions and parameters of the three paramagnetic tags were fitted. A good correlation between experimental data and predicted values was found, validating the approach. The study establishes that pseudocontact shifts can readily be applied to multimeric proteins, offering new perspectives for studies of large protein complexes by paramagnetic NMR spectroscopy.

Structure-Activity Relationship Studies of Pyridine-Based Ligands and Identification of a Fluorinated Derivative for Positron Emission Tomography Imaging of Cannabinoid Type 2 Receptors.

The cannabinoid type 2 (CB2) receptor has emerged as a valuable target for therapy and imaging of immune-mediated pathologies. With the aim to find a suitable radiofluorinated analogue of the previously reported CB2 positron emission tomography (PET) radioligand [11C]RSR-056, 38 fluorinated derivatives were synthesized and tested by in vitro binding assays. With a Ki (hCB2) of 6 nM and a selectivity factor of nearly 700 over cannabinoid type 1 receptors, target compound 3 exhibited optimal in vitro properties and was selected for evaluation as a PET radioligand. [18F]3 was obtained in an average radiochemical yield of 11 ± 4% and molar activities between 33 and 114 GBq/µmol. Specific binding of [18F]3 to CB2 was demonstrated by in vitro autoradiography and in vivo PET experiments using the CB2 ligand GW-405 833. Metabolite analysis revealed only intact [18F]3 in the rat brain. [18F]3 detected CB2 upregulation in human amyotrophic lateral sclerosis spinal cord tissue and may thus become a candidate for diagnostic use in humans.

Mechanical Stabilization of Helical Chirality in a Macrocyclic Oligothiophene.

We introduce a design principle to stabilize helically chiral structures from an achiral tetrasubstituted [2.2]paracyclophane by integrating it into a macrocycle. The [2.2]paracyclophane introduces a three-dimensional perturbation into a nearly planar macrocyclic oligothiophene. The resulting helical structure is stabilized by two bulky substituents installed on the [2.2]paracyclophane unit. The increased enantiomerization barrier enabled the separation of both enantiomers. The synthesis of the target helical macrocycle 1 involves a sequence of halogenation and cross-coupling steps and a high-dilution strategy to close the macrocycle. Substituents tuning the energy of the enantiomerization process can be introduced in the last steps of the synthesis. The chiral target compound 1 was fully characterized by NMR spectroscopy and mass spectrometry. The absolute configurations of the isolated enantiomers were assigned by comparing the data of circular dichroism spectroscopy with TD-DFT calculations. The enantiomerization dynamics was studied by dynamic HPLC and variable-temperature 2D exchange spectroscopy and supported by quantum-chemical calculations.