Exploration Of Chemical Space Research Articles

Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability

The exploration of chemical space holds promise for developing influential chemical entities. Molecular representations, which reflect features of molecular structure in silico, assist in navigating chemical space appropriately. Unlike atom-level molecular representations, such as SMILES and atom graph, which can sometimes lead to confusing interpretations about chemical substructures, substructure-level molecular representations encode important substructures into molecular features; they not only provide more information for predicting molecular properties and drug‒drug interactions but also help to interpret the correlations between molecular properties and substructures. However, it remains challenging to represent the entire molecular structure both intactly and simply with substructure-level molecular representations. In this study, we developed a novel substructure-level molecular representation and named it a group graph. The group graph offers three advantages: (a) the substructure of the group graph reflects the diversity and consistency of different molecular datasets; (b) the group graph retains molecular structural features with minimal information loss because the graph isomorphism network (GIN) of the group graph performs well in molecular properties and drug‒drug interactions prediction, showing higher accuracy and efficiency than the model of other molecular graphs, even without any pretraining; and (c) the molecular property may change when the substructure is substituted with another of differing importance in group graph, facilitating the detection of activity cliffs. In addition, we successfully predicted structural modifications to improve blood‒brain barrier permeability (BBBP) via the GIN of group graph. Therefore, the group graph takes advantages for simultaneously representing molecular local characteristics and global features.Scientific contribution The group graph, as a substructure-level molecular representation, has the ability to retain molecular structural features with minimal information loss. As a result, it shows superior performance in predicting molecular properties and drug‒drug interactions with enhanced efficiency and interpretability.

ISIM-sigma: efficient standard deviation calculation for molecular similarity.

The average and variance of the molecular similarities in a set is high-value and useful information for cheminformatics tasks like chemical space exploration and subset selection. However, the calculation of the variance of the complete similarity matrix has a quadratic complexity, O ( N 2 ). As the sizes of molecular libraries constantly increase, this pairwise approach is unfeasible. In this work, we present an alternative to obtaining the exact standard deviation of the molecular similarities in a set (with N molecules and M features) for the Russell-Rao (RR) and Sokal-Michener (SM) similarity indexes in O ( N M 2 ) complexity. Additionally, we present a highly accurate approximation with linear complexity, O ( N ), based on the sampling of representative molecules from the set. The proposed approximation can be extended to other similarity indexes, including the popular Jaccard-Tanimoto (JT). With only the sampling of 50 molecules, the proposed method can estimate the standard deviation of the similarities in a set with RMSE lower than 0.01 for sets of up to 50,000 molecules. In comparison, random sampling does not warrant a good approximation as shown in our results.

AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning.

Graphs are one of the most natural and powerful representations available for molecules; natural because they have an intuitive correspondence to skeletal formulas, the language used by chemists worldwide, and powerful, because they are highly expressive both globally (molecular topology) and locally (atom and bond properties). Graph kernels are used to transform molecular graphs into fixed-length vectors, which, based on their capacity of measuring similarity, can be used as fingerprints for machine learning (ML). To date, graph kernels have mostly focused on the atomic nodes of the graph. In this work, we developed a graph kernel based on atom-atom, bond-bond, and bond-atom (AABBA) autocorrelations. The resulting vector representations were tested on regression ML tasks on a data set of transition metal complexes; a benchmark motivated by the higher complexity of these compounds relative to organic molecules. In particular, we tested different flavors of the AABBA kernel in the prediction of the energy barriers and bond distances of the Vaska's complex data set (Friederich et al., Chem. Sci., 2020, 11, 4584). For a variety of ML models, including neural networks, gradient boosting machines, and Gaussian processes, we showed that AABBA outperforms the baseline including only atom-atom autocorrelations. Dimensionality reduction studies also showed that the bond-bond and bond-atom autocorrelations yield many of the most relevant features. We believe that the AABBA graph kernel can accelerate the exploration of large chemical spaces and inspire novel molecular representations in which both atomic and bond properties play an important role.

ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning

Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like click chemistry to assemble molecules and incorporates reinforcement learning along with inpainting technique to ensure that the proposed molecules display high diversity, novelty and strong binding tendency. ClickGen demonstrates superior performance over the other reaction-based generative models in terms of novelty, synthesizability, and docking conformation similarity for existing binders targeting the three proteins. We then proceeded to conduct wet-lab validation on the ClickGen’s proposed molecules for poly adenosine diphosphate-ribose polymerase 1. Due to the guaranteed high synthesizability and model-generated synthetic routes for reference, we successfully produced and tested the bioactivity of these novel compounds in just 20 days, much faster than typically expected time frame when handling sufficiently novel molecules. In bioactivity assays, two lead compounds demonstrated superior anti-proliferative efficacy against cancer cell lines, low toxicity, and nanomolar-level inhibitory activity to PARP1. We demonstrate that ClickGen and related models may represent a new paradigm in molecular generation, bringing AI-driven, automated experimentation and closed-loop molecular design closer to realization.

Development of Calcium-Dependent Phospholipase A2 Inhibitors to Target Cellular Senescence and Oxidative Stress in Neurodegenerative Diseases.

Significance: Cellular senescence is a critical process underlying aging and is associated with age-related diseases such as Alzheimer's disease. Lipids are implicated in cellular senescence. Fatty acids, particularly eicosanoids, have been associated with various forms of senescence and inflammation, and the associated reactive oxygen species production has been proposed as a therapeutic target for mitigating senescence. When overactivated, calcium-dependent phospholipase A2 (cPLA2) catalyzes the conversion of arachidonic acid into eicosanoids such as leukotrienes and prostaglandins. Recent Advances: With a growing understanding of the importance of lipids as mediators and modulators of senescence, cPLA2 has emerged as a compelling drug target. cPLA2 overactivation plays a significant role in several pathways associated with senescence, including neuroinflammation and oxidative stress. Critical Issues: Previous cPLA2 inhibitors have shown potential in ameliorating inflammation and oxidative stress, but the dominant hurdles in the central nervous system-targeting drug discovery are specificity and blood-brain barrier penetrance. Future Directions: With the need for more effective drugs against neurological diseases, we emphasize the significance of discovering new brain-penetrant, potent, and specific cPLA2 inhibitors. We discuss how the recently developed Virtual Synthon Hierarchical Enumeration Screening, an iterative synthon-based approach for fast structure-based virtual screening of billions of compounds, provides an efficient exploration of large chemical spaces for the discovery of brain-penetrant cPLA2 small-molecule inhibitors. Antioxid. Redox Signal. 00, 000-000.

Rapid Exploration of Chemical Space by High-Throughput Desorption Electrospray Ionization Mass Spectrometry.

This study leverages accelerated reactions at the solution/air interface of microdroplets generated by desorption electrospray ionization (DESI) to explore the chemical space. DESI is utilized to synthesize drug analogs at an overall rate of 1 reaction mixture per second, working on the low-nanogram scale. Transformations of multiple drug molecules at specific functionalities (phenol, hydroxyl, amino, carbonyl, phenyl, thiophenyl, and alkenyl) are achieved using electrophilic/nucleophilic, redox, C-H functionalization, and coupling reactions. These transformations occur under ambient conditions on the millisecond time scale with direct detection of products being successful in all but three of the reaction types studied. The large scope (22 bioactive compounds, >20 chemical transformations, and >300 functionalization reagents) and high speed (>3000 reactions/hour) provide access to a wide array of drug analogs that can be used for bioactivity testing. A total of ∼6800 unique reactions were examined through a data-driven workflow, and more than 3000 unique derivatives (∼44%) were identified tentatively by the m/z value and signal-to-control ratio in single-stage mass spectrometry (MS) analysis, with over 1000 being further characterized by tandem MS. The speed of the DESI-MS reaction screen provides potential advantages for emerging machine learning-based predictions of organic synthesis, and it sets the stage for future online DESI-MS bioassays and scaled-up microdroplet synthesis before formal characterization of hit compounds is sought using traditional methods of drug discovery.

Advancement of Artificial Intelligence in Drug Discovery: A Comprehensive Review

Abstract: The traditional drug discovery process is notoriously time-consuming, expensive, and damaged, with high failure rates. However, the recent surge in Artificial Intelligence (AI) has presented itself as a game-changer in this field. This comprehensive review delves into the profound impact of AI on various aspects of drug discovery, encompassing crucial stages like target identification, molecular analysis, compound screening, and even drug development. Machine learning algorithms are pivotal in analyzing vast datasets to predict important aspects of potential drug candidates. These predictions include their pharmacokinetic properties (how the body absorbs and eliminates them) and possible toxicity, effectively streamlining the process and reducing risks associated with further development. Additionally, AI facilitates the exploration of vast chemical spaces, enabling the design and synthesis of novel drug candidates with enhanced efficacy and specificity. The review highlights AI's transformative potential in drug discovery and acknowledges the existing challenges. Concerns surrounding data quality, interpretability of AI models, and ethical considerations are addressed, paving the way for responsible development and integration of AI within the pharmaceutical industry. Ultimately, this review underscores AI's massive potential in revolutionizing drug discovery, offering a path towards faster development of life-saving treatments and fostering healthcare advancements.

NeuroClick: Advanced Software for Designing Blood-Brain Barrier-Permeable Drugs Using Click Reaction Simulations.

NeuroClick is a software tool designed for the in silico execution of azide-alkyne cycloaddition reactions, commonly known as click reactions. We developed this graphical user interface application to expedite the drug discovery process by generating libraries of 1,2,3-triazole compounds. NeuroClick enables users to input reagent SMILES strings, rapidly generating and screening extensive combinatorial libraries at a pace of 10,000 molecules per minute. The software applies stringent criteria to ensure the relevance and accuracy of the generated compounds, excluding molecules without azide groups or those with multiple reactive functional groups to maintain dataset integrity. NeuroClick incorporates advanced filtering options based on Lipinski's rule of five and blood-brain barrier (BBB) permeability predictors, allowing researchers to identify drug-like molecules with potential central nervous system activity. The software's high-throughput and user-friendly interface significantly enhance the efficiency of early-stage drug development by facilitating the exploration of vast chemical spaces and identifying promising lead compounds for further development. This article provides comprehensive guidance on the installation, usage, and features of NeuroClick, ensuring that users can leverage its full potential in their research endeavors. © 2024 Wiley Periodicals LLC. Basic Protocol: NeuroClick workflow for generating BBB-permeating drugs.

Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-[formula omitted]B receptors based on machine learning and molecular docking

Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the four subgroups of breast cancer “hormone receptor-positive and HER2-negative, hormone receptor-positive and HER2-positive, hormone receptor-negative and HER2-positive, and hormone receptor-negative and HER2-negative” it is crucial to inhibit specific targets such as EGFR, HER2, ER, NF-κB, and PR.In this study, we evaluated various methods for binary and multiclass classification. Among them, the GA-SVM-SVM:GA-SVM-SVM model was selected with an accuracy of 0.74, an F1-score of 0.73, and an AUC of 0.92 for virtual screening of ligands from the BindingDB database. This model successfully identified 4454, 803, 438, and 378 ligands with over 90% precision in both active/inactive and target prediction for the classes of EGFR+HER2, ER, NF-κB, and PR, respectively, from the BindingDB database. Based on to the selected ligands, we created a dendrogram that categorizes different ligands based on their targets. This dendrogram aims to facilitate the exploration of chemical space for various therapeutic targets.Ligands that surpassed a 90% threshold in the product of activity probability and correct target selection probability were chosen for further investigation using molecular docking. The binding energy range for these ligands against their respective targets was calculated to be between −15 and −5 kcal/mol. Finally, based on general and common rules in medicinal chemistry, we selected 2, 3, 3, and 8 new ligands with high priority for further studies in the EGFR+HER2, ER, NF-κB, and PR classes, respectively.

Machine Learnable Language for the Chemical Space of Nanopores Enables Structure-Property Relationships in Nanoporous 2D Materials.

The synthesis of nanoporous two-dimensional (2D) materials has revolutionized fields such as membrane separations, DNA sequencing, and osmotic power harvesting. Nanopores in 2D materials significantly modulate their optoelectronic, magnetic, and barrier properties. However, the large number of possible nanopore isomers makes their study onerous, while the lack of machine-learnable representations stymies progress toward structure-property relationships. Here, we develop a language for nanopores in 2D materials, called STring Representation Of Nanopore Geometry (STRONG), that opens the field of 2D nanopore informatics. We show that STRONGs are naturally suited for machine learning via recurrent neural networks, predicting formation energies/times of arbitrary nanopores and transport barriers for CO2, N2, and O2 gas molecules, enabling structure-property relationships. The machine learning models enable the discovery of specific nanopore topologies to separate CO2/N2, O2/CO2, and O2/N2 gas mixtures with high selectivity ratios. We also enable the rapid enumeration of unique configurations of stable, functionalized nanopores in 2D materials via STRONGs, allowing systematic searching of the vast chemical space of nanopores. Using the STRONGs approach, we find that a mix of hydrogen and quinone functionalization results in the most stable functionalized nanopore configuration in graphene, a discovery made feasible by expedited chemical space exploration. Additionally, we also unravel the STRONGs approach as ∼1000 times faster than graph theory algorithms to distinguish nanopore shapes. These advances in the language-based representation of 2D nanopores will accelerate the tailored design of nanoporous materials.

Exploration of Chemical Space through Automated Reasoning

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts. Comgen accepts a variety of user‐specified criteria, converts these into an abstract form, and utilises a powerful automated reasoning algorithm to identify compositions that satisfy these user requirements, or prove that the requirements cannot be simultaneously satisfied. In contrast to machine learning techniques, explicitly reasoning about domain knowledge, rather than making inferences from data, ensures that Comgen’s outputs are fully interpretable and provably correct. Users interact with Comgen through a high‐level Python interface. We illustrate use of the tool with several case studies focused on the search for new ionic conductors. Further, we demonstrate the integration of Comgen into an end‐to‐end automated workflow to propose and evaluate candidate compositions quantitatively, prior to experimental investigation.

Exploration of Chemical Space through Automated Reasoning.

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts. Comgen accepts a variety of user-specified criteria, converts these into an abstract form, and utilises a powerful automated reasoning algorithm to identify compositions that satisfy these user requirements, or prove that the requirements cannot be simultaneously satisfied. In contrast to machine learning techniques, explicitly reasoning about domain knowledge, rather than making inferences from data, ensures that Comgen's outputs are fully interpretable and provably correct. Users interact with Comgen through a high-level Python interface. We illustrate use of the tool with several case studies focused on the search for new ionic conductors. Further, we demonstrate the integration of Comgen into an end-to-end automated workflow to propose and evaluate candidate compositions quantitatively, prior to experimental investigation.

Semi-synthesis of a DNA-Tagged Polyketide-Peptide Hybrid Macrocycle Using a Biosynthetically Prepared Fungal Macrolide as a Synthetic Component.

Presented herein is a synthetic biological method using genome mining and heterologous expression systems that provides access to natural products with desirable structural features as building blocks. In this investigation, we synthesized polyketide-peptide hybrid macrocycles with DNA tags, which have the potential to access a DNA-encoded library containing over one million compounds. This study demonstrates that synthetic biology offers a tool for expanding the diversity of building blocks, facilitating the exploration of unexplored chemical space.

Identification of novel and potent inhibitors of SARS-CoV-2 main protease from DNA-encoded chemical libraries.

In vitro screening of large compound libraries with automated high-throughput screening is expensive and time-consuming and requires dedicated infrastructures. Conversely, the selection of DNA-encoded chemical libraries (DECLs) can be rapidly performed with routine equipment available in most laboratories. In this study, we identified novel inhibitors of SARS-CoV-2 main protease (Mpro) through the affinity-based selection of the DELopen library (open access for academics), containing 4.2 billion compounds. The identified inhibitors were peptide-like compounds containing an N-terminal electrophilic group able to form a covalent bond with the nucleophilic Cys145 of Mpro, as confirmed by x-ray crystallography. This DECL selection campaign enabled the discovery of the unoptimized compound SLL11 (IC50 = 30 nM), proving that the rapid exploration of large chemical spaces enabled by DECL technology allows for the direct identification of potent inhibitors avoiding several rounds of iterative medicinal chemistry. As demonstrated further by x-ray crystallography, SLL11 was found to adopt a highly unique U-shaped binding conformation, which allows the N-terminal electrophilic group to loop back to the S1' subsite while the C-terminal amino acid sits in the S1 subsite. MP1, a close analog of SLL11, showed antiviral activity against SARS-CoV-2 in the low micromolar range when tested in Caco-2 and Calu-3 (EC50 = 2.3 µM) cell lines. As peptide-like compounds can suffer from low cell permeability and metabolic stability, the cyclization of the compounds will be explored in the future to improve their antiviral activity.

Traversing chemical space with active deep learning for low-data drug discovery.

Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, in terms of either size or molecular diversity. Active deep learning has high potential for low-data drug discovery, as it allows iterative model improvement during the screening process. However, there are several 'known unknowns' that limit the wider adoption of active deep learning in drug discovery: (1) what the best computational strategies are for chemical space exploration, (2) how active learning holds up to traditional, non-iterative, approaches and (3) how it should be used in the low-data scenarios typical of drug discovery. To provide answers, this study simulates a low-data drug discovery scenario, and systematically analyzes six active learning strategies combined with two deep learning architectures, on three large-scale molecular libraries. We identify the most important determinants of success in low-data regimes and show that active learning can achieve up to a sixfold improvement in hit discovery when compared with traditional screening methods.

Oxadiazolines as Photoreleasable Labels for Drug Target Identification.

Photoaffinity labeling is a widely used technique for studying ligand-protein and protein-protein interactions. Traditional photoaffinity labels utilize nonspecific C-H bond insertion reactions mediated by a highly reactive intermediate. Despite being the most widely used photoaffinity labels, diazirines exhibit limited compatibility with downstream organic reactions and suffer from storage stability concerns. This study introduces oxadiazolines as innovative and complementary photoactivatable labels for addition to the toolbox and demonstrates their application in vitro and through in cellulo labeling experiments. Oxadiazolines can be easily synthesized from ketone moieties and cleaved with 302-330 nm light to cleanly liberate a diazo reactive moiety that can covalently modify nucleophilic amino acid residues. Notably, oxadiazolines are compatible with various organic reaction conditions and functional groups, allowing for the exploration of a large chemical space. Several known inhibitors featuring the oxadiazoline functionality were prepared without affecting their binding affinity. Furthermore, we confirmed the ability of oxadiazolines to form covalent bonds with proteins upon UV-irradiation, both in vitro and in cellulo, yielding comparable results to those of the matched diazirine compounds.

Navigation through high-dimensional chemical space: discovery of Ba5Y13[SiO4]8O8.5 and Ba3Y2[Si2O7]2.

Two compounds were discovered in the well-studied BaO-Y2O3-SiO2 phase field. Two different experimental routines were used for the exploration of this system due to the differences of synthetic conditions and competition with a glass field. The first phase Ba5Y13[SiO4]8O8.5 was isolated through a combination of energy dispersive X-ray spectroscopy analysis and diffraction techniques which guided the exploration. The second phase Ba3Y2[Si2O7]2 was located using iterative algorithmic identification of target compositions. The structure solution of the new compounds was aided by continuous rotation electron diffraction, and the structures were refined against combined synchrotron and neutron time-of-flight powder diffraction. Ba5Y13[SiO4]8O8.5 crystallizes in I4̄2m, a = 18.92732(1), c = 5.357307(6) Å and represents its own structure type which combines elements of structures of known silicates embedded in columns of interconnected yttrium-centred polyhedra characteristic of high-pressure phases. Ba3Y2[Si2O7]2 has P21 symmetry with a pseudo-tetragonal cell (a = 16.47640(4), b = 9.04150(5), c = 9.04114(7) Å, β = 90.0122(9)°) and is a direct superstructure of the Ca3BaBi[P2O7]2 structure. Despite the lower symmetry, the structure of Ba3Y2[Si2O7]2 retains disorder in both Ba/Y sites and disilicate network, thus presenting a superposition of possible locally-ordered fragments. Ba5Y13[SiO4]8O8.5 has low thermal conductivity of 1.04(5) W m-1 K-1 at room temperature. The two discovered phases provide a rich structural platform for further functional material design. The interplay of automated unknown phase composition identification with multiple diffraction methods offers acceleration of the time-consuming exploration of high-dimensional chemical spaces for new structures.

Challenges Reconciling Theory and Experiments in the Prediction of Lattice Thermal Conductivity: The Case of Cu-Based Sulvanites.

The exploration of large chemical spaces in search of new thermoelectric materials requires the integration of experiments, theory, simulations, and data science. The development of high-throughput strategies that combine DFT calculations with machine learning has emerged as a powerful approach to discovering new materials. However, experimental validation is crucial to confirm the accuracy of these workflows. This validation becomes especially important in understanding the transport properties that govern the thermoelectric performance of materials since they are highly influenced by synthetic, processing, and operating conditions. In this work, we explore the thermal conductivity of Cu-based sulvanites by using a combination of theoretical and experimental methods. Previous discrepancies and significant variations in reported data for Cu3VS4 and Cu3VSe4 are explained using the Boltzmann Transport Equation for phonons and by synthesizing well-characterized defect-free samples. The use of machine learning approaches for extracting high-order force constants opens doors to charting the lattice thermal conductivity across the entire Cu-based sulvanite family-finding not only materials with κ l values below 2 W m-1 K-1 at moderate temperatures but also rationalizing their thermal transport properties based on chemical composition.

Molecular similarity: Theory, applications, and perspectives

Molecular similarity pervades much of our understanding and rationalization of chemistry. This has become particularly evident in the current data-intensive era of chemical research, with similarity measures serving as the backbone of many Machine Learning (ML) supervised and unsupervised procedures. Here, we present a discussion on the role of molecular similarity in drug design, chemical space exploration, chemical “art” generation, molecular representations, and many more. We also discuss more recent topics in molecular similarity, like the ability to efficiently compare large molecular libraries.

Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.

The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. However, current molecular representation models rarely consider the three-dimensional conformational space of molecules, losing sight of the dynamic nature of small molecules as well as the essence of molecular conformational space that covers the heterogeneity of molecule properties, such as the multi-target mechanism of action, recognition of different biomolecules, dynamics in cytoplasm and membrane. In this study, a new model named GeminiMol is proposed to incorporate conformational space profiles into molecular representation learning, which extracts the feature of capturing the complicated interplay between the molecular structure and the conformational space. Although GeminiMol is pre-trained on a relatively small-scale molecular dataset (39290 molecules), it shows balanced and superior performance not only on 67 molecular properties predictions but also on 73 cellular activity predictions and 171 zero-shot tasks (including virtual screening and target identification). By capturing the molecular conformational space profile, the strategy paves the way for rapid exploration of chemical space and facilitates changing paradigms for drug design.