Digitization of molecular complexity with machine learning

Digitization of molecular complexity is of key importance in chemistry and life sciences to develop structure-activity relationships in chemical behavior and biological activity. The complexity of a given molecule compared to others is largely based on intuitive perception and lacks a standardized numerical measure. Quantifying molecular complexity remains a fundamental challenge, with key implications currently remaining controversial. In this study, we introduce a novel machine learning-based framework employing a Learning to Rank (LTR) approach to quantify molecular complexity on the basis of labeled data. As a result, we developed a ranking model utilizing the dataset that comprizes approximately 300 000 data points across diverse chemical structures, leveraging human expertise to capture complex decision rules that researchers intuitively use. Applications of our model in mapping the current organic chemistry landscape, analyzing FDA-approved drugs, guiding lead optimization processes, and interpreting total synthesis approaches reveal key trends in increasing molecular complexity and synthetic strategy evolution. Our study advances the methodologies available for quantifying molecular complexity, changing it from an elusive property to a numerical characteristic. With machine learning, we managed to digitize human perception of molecular complexity. Moreover, a corresponding large labeled dataset was produced for future research in this area.

Superimposing Ligands with a Ligand Overlay as an Alternate Topology Model for λ-Dynamics-Based Calculations

Alchemical free energy (AFE) calculations can predict binding affinity changes as a function of structural modifications and have become powerful tools for lead optimization and drug discovery. Central to the setup and performance of AFE calculations is the manner of mapping alchemical transformations, known as the topology model. Single, dual, and hybrid topology models have been used with various AFE methods in the field. In recent works, λ-dynamics (λD) free energy calculations, specifically, have preferred the use of a hybrid multiple topology (HMT) for sampling multiple ligand perturbations. In this work, we evaluate a new topology method called ligand overlay (LO) for use with λD-based calculations, including the recently introduced λ-dynamics with a bias-updated Gibbs sampling (LaDyBUGS) approach. LO is a full multiple topology model that allows entire ligands to be sampled and restrained within a λ-dynamics framework. Relative binding free energies were computed with HMT or LO topology models with LaDyBUGS for 45 ligands across five protein benchmark systems. An overall Pearson R correlation of 0.98 and mean unsigned error of 0.32 kcal/mol were observed, suggesting that LO is a viable alternative topology model for λD-based calculations. We discuss the merits of using an HMT or LO model for future ligand studies with λD or LaDyBUGS calculations.

Derivatization of 2,1,3-Benzothiadiazole via Regioselective C-H Functionalization and Aryne Reactivity

Despite growing interest in 2,1,3-benzothiadiazole (BTD) as an integral component of many functional molecules, methods for the functionalization of its benzenoid ring have remained limited, and many even simply decorated BTDs have required de novo synthesis. We show that regioselective Ir-catalyzed C-H borylation allows access to versatile 5-boryl or 4,6-diboryl BTD building blocks, which undergo functionalization at the C4, C5, C6, and C7 positions. The optimization and regioselectivity of C-H borylation are discussed. A broad reaction scope is presented, encompassing ipso substitution at the C-B bond, the first examples of ortho-directed C-H functionalization of BTD, ring closing reactions to generate fused ring systems, as well as the generation and capture reactions of novel BTD-based heteroarynes. The regioselectivity of the latter is discussed with reference to the Aryne Distortion Model.

Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein-ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.

Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling

Relative binding free energy calculations have become an integral computational tool for lead optimization in structure-based drug design. Classical alchemical methods, including free energy perturbation or thermodynamic integration, compute relative free energy differences by transforming one molecule into another. However, these methods have high operational costs due to the need to perform many pairwise perturbations independently. To reduce costs and accelerate molecular design workflows, we present a method called λ-dynamics with bias-updated Gibbs sampling. This method uses dynamic biases to continuously sample between multiple ligand analogues collectively within a single simulation. We show that many relative binding free energies can be determined quickly with this approach without compromising accuracy. For five benchmark systems, agreement to experiment is high, with root mean square errors near or below 1.0 kcal mol-1. Free energy results are consistent with other computational approaches and within statistical noise of both methods (0.4 kcal mol-1 or less). Notably, large efficiency gains over thermodynamic integration of 18-66-fold for small perturbations and 100-200-fold for whole aromatic ring substitutions are observed. The rapid determination of relative binding free energies will enable larger chemical spaces to be more readily explored and structure-based drug design to be accelerated.

Synthesis, crystal structure, DFT, Hirshfeld surface analysis, energy frameworks and in-Silico drug-targeting PFKFB3 kinase of novel triazolequinoxalin derivative (TZQ) as a therapeutic Strategy against cancer

Overall, drug design is a dynamic and evolving field, with researchers constantly working to improve their understanding of molecular interactions, develop new computational methods, and explore innovative techniques for creating effective and safe medications. The process can involve steps such as the identification of targets, the discovery of lead compounds, lead optimization, preliminary testing, human trials, regulatory approval and finally post-marketing surveillance, all aimed at bringing a new drug from concept to market. In this article, the synthesis of the novel triazolequinoxalin (TZQ) 1-((1-hexyl-1H-1,2,3-triazol-5-yl)methyl)-3-phenylquinoxalin-2(1H)-one (4) is reported. The structure has been identified with a variety of spectroscopic methods (1H, 13C NMR, and LC-MS) and finally, the structure has been determined by X-ray diffraction (XRD) studies. The TZQ molecule has crystallized in the monoclinic space C2/c group with unit cell dimensions a = 41.201(2) Å, b = 10.6339(6) Å, c = 9.4997(4) Å, β = 93.904(4). The crystal structure is stabilized by intermolecular interactions (N-H ⋯ O and N-H … Cg) occurring within the molecule. The presence of these intermolecular interactions is evaluated through analysis of Hirshfeld surfaces (HS) and two-dimensional (2D) chemical fingerprints map. Additionally, energy frameworks were employed to identify the prevailing interaction energy influencing the molecular arrangement. Density Functional Theory (DFT) calculations were computed to establish concurrence between theoretical and experimental results. Furthermore, the HOMO-LUMO energy levels were determined using the B3LYP/6-31+G(d, p) level of theory. Finally, molecular docking was used to predict the anti-cancer activity of the compound (4) against PFKFB3 kinase and presented noticeable hydrophilic and hydrophobic interactions at the active site region.

Most protein domains exist as variants with distinct functions across cells, tissues and diseases

Protein domains are the active subunits that provide proteins with specific functions through precise three-dimensional structures. Such domains facilitate most protein functions, including molecular interactions and signal transduction. Currently, these protein domains are described and analyzed as invariable molecular building blocks with fixed functions. Here, I show that most human protein domains exist as multiple distinct variants termed 'domain isotypes'. Domain isotypes are used in a cell, tissue and disease-specific manner and have surprisingly different 3D structures. Accordingly, domain isotypes, compared to each other, modulate or abolish the functionality of protein domains. These results challenge the current view of protein domains as invariable building blocks and have significant implications for both wet- and dry-lab workflows. The extensive use of protein domain isotypes within protein isoforms adds to the literature indicating we need to transition to an isoform-centric research paradigm.

A high-throughput screening campaign against PFKFB3 identified potential inhibitors with novel scaffolds

The growth of solid tumors depends on tumor vascularization and the endothelial cells (ECs) that line the lumen of blood vessels. ECs generate a large fraction of ATP through glycolysis, and elevation of their glycolytic activity is associated with angiogenic behavior in solid tumors. 6-Phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) positively regulates glycolysis via fructose-2/6-bisphosphate, the product of its kinase activity. Partial inhibition of glycolysis in tumor ECs by targeting PFKFB3 normalizes the otherwise abnormal tumor vessels, thereby reducing metastasis and improving the outcome of chemotherapy. Although a limited number of tool compounds exist, orally available PFKFB3 inhibitors are unavailable. In this study we conducted a high-throughput screening campaign against the kinase activity of PFKFB3, involving 250,240 chemical compounds. A total of 507 initial hits showing >50% inhibition at 20 µM were identified, 66 of them plus 1 analog from a similarity search consistently displayed low IC50 values (<10 µM). In vitro experiments yielded 22 nontoxic hits that suppressed the tube formation of primary human umbilical vein ECs at 10 µM. Of them, 15 exhibited binding affinity to PFKFB3 in surface plasmon resonance assays, including 3 (WNN0403-E003, WNN1352-H007 and WNN1542-F004) that passed the pan-assay interference compounds screening without warning flags. This study provides potential leads to the development of new PFKFB3 inhibitors.

Free Energy Calculations Using the Movable Type Method with Molecular Dynamics Driven Protein–Ligand Sampling

Fast and accurate biomolecular free energy estimation has been a significant interest for decades, and with recent advances in computer hardware, interest in new method development in this field has even grown. Thorough configurational state sampling using molecular dynamics (MD) simulations has long been applied to the estimation of the free energy change corresponding to the receptor-ligand complexing process. However, performing large-scale simulation is still a computational burden for the high-throughput hit screening. Among molecular modeling tools, docking and scoring methods are widely used during the early stages of the drug discovery process in that they can rapidly generate discrete receptor-ligand binding modes and their individual binding affinities. Unfortunately, the lack of thorough conformational sampling in docking and scoring protocols leads to difficulty discovering global minimum binding modes on a complicated energy landscape. The Movable Type (MT) method is a novel absolute binding free energy approach which has demonstrated itself to be robust across a wide range of targets and ligands. Traditionally, the MT method is used with protein-ligand binding modes generated with rigid-receptor or flexible-receptor (induced fit) docking protocols; however, these protocols are by their nature less likely to be effective with more highly flexible targets or with those situations in which binding involves multiple step pathways. In these situations, more thorough samplings are required to better explain the free energy of binding. Therefore, to explore the prediction capability and computational efficiency of the MT method when using more thorough protein-ligand conformational sampling protocols, in the present work, we introduced a series of binding mode modeling protocols ranging from conventional docking routines to single-trajectory conventional molecular dynamics (cMD) and parallel Monte Carlo molecular dynamics (MCMD). Through validation against several structurally and mechanistically diverse protein-ligand test sets, we explore the performance of the MT method as a virtual screening tool to work with the docking protocols and as an MD simulation-based binding free energy tool.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1]

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.

Structural derivatization strategies of natural phenols by semi-synthesis and total-synthesis

Structural derivatization of natural products has been a continuing and irreplaceable source of novel drug leads. Natural phenols are a broad category of natural products with wide pharmacological activity and have offered plenty of clinical drugs. However, the structural complexity and wide variety of natural phenols leads to the difficulty of structural derivatization. Skeleton analysis indicated most types of natural phenols can be structured by the combination and extension of three common fragments containing phenol, phenylpropanoid and benzoyl. Based on these fragments, the derivatization strategies of natural phenols were unified and comprehensively analyzed in this review. In addition to classical methods, advanced strategies with high selectivity, efficiency and practicality were emphasized. Total synthesis strategies of typical fragments such as stilbenes, chalcones and flavonoids were also covered and analyzed as the supplementary for supporting the diversity-oriented derivatization of natural phenols.

Discovery and development of tumor glycolysis rate-limiting enzyme inhibitors

Tumor cells mainly provide necessary energy and substances for rapid cell growth through aerobic perglycolysis rather than oxidative phosphorylation. This phenomenon is called the "Warburg effect". The mechanism of glycolysis in tumor cells is more complicated, which is caused by the comprehensive regulation of multiple factors. Abnormal enzyme metabolism is one of the main influencing factors and inhibiting the three main rate-limiting enzymes in glycolysis is thought to be important strategy for cancer treatment. Therefore, numerous inhibitors of glycolysis rate-limiting enzyme have been developed in recent years, such as the latest HKII inhibitor and PKM2 inhibitor Pachymic acid (PA) and N-(4-(3-(3-(methylamino)-3-oxopropyl)-5-(4'-(trifluoromethyl)-[1,1'-biphenyl]-4-yl)-1H-pyrazol-1-yl)phenyl)propiolamide. The review focuses on source, structure-activity relationship, bioecological activity and mechanism of the three main rate-limiting enzymes inhibitors, and hopes to guide the future research on the design and synthesis of rate-limiting enzyme inhibitors.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

PFKFB3 inhibitors as potential anticancer agents: Mechanisms of action, current developments, and structure-activity relationships

Cancer cells adopt aerobic glycolysis as the major source of energy and biomass production for fast cell proliferation. The bifunctional enzyme, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), plays a crucial role in the regulation of glycolysis by controlling the steady-state cytoplasmic levels of fructose-2,6-bisphosphate (F2,6BP), which is the most potent allosteric activator of 6-phosphofructo-1-kinase (PFK-1), a key rate-limiting enzyme of glycolysis. Therefore, selective inhibition of PFKFB3 has gained substantial interest as an attractive strategy for cancer therapy. In recent years, numerous class PFKFB3 inhibitors have been disclosed, and emerging trends such as the availability of PFKFB3 crystal structures, structure-based screening strategies and diverse functional assays are improving optimization and development of original leads. Herein, we review the structure and function of PFKFB3 as well as the representative small-molecule inhibitors, in particular emphasis on their chemical structures, pharmacological properties, selectivity, binding modes and structure-activity relationships (SARs).

Tuning PFKFB3 Bisphosphatase Activity Through Allosteric Interference

The human inducible phospho-fructokinase bisphosphatase isoform 3, PFKFB3, is a crucial regulatory node in the cellular metabolism. The enzyme is an important modulator regulating the intracellular fructose-2,6-bisphosphate level. PFKFB3 is a bifunctional enzyme with an exceptionally high kinase to phosphatase ratio around 740:1. Its kinase activity can be directly inhibited by small molecules acting directly on the kinase active site. On the other hand, here we propose an innovative and indirect strategy for the modulation of PFKFB3 activity, achieved through allosteric bisphosphatase activation. A library of small peptides targeting an allosteric site was discovered and synthesized. The binding affinity was evaluated by microscale thermophoresis (MST). Furthermore, a LC-MS/MS analytical method for assessing the bisphosphatase activity of PFKFB3 was developed. The new method was applied for measuring the activation on bisphosphatase activity with the PFKFB3-binding peptides. The molecular mechanical connection between the newly discovered allosteric site to the bisphosphatase activity was also investigated using both experimental and computational methods.