Early Drug Discovery Research Articles

Thermal unfolding methods in drug discovery.

Thermal unfolding methods, applied in both isolated protein and cell-based settings, are increasingly used to identify and characterize hits during early drug discovery. Technical developments over recent years have facilitated their application in high-throughput approaches, and they now are used more frequently for primary screening. Widespread access to instrumentation and automation, the ability to miniaturize, as well as the capability and capacity to generate the appropriate scale and quality of protein and cell reagents have all played a part in these advances. As the nature of drug targets and approaches to their modulation have evolved, these methods have broadened our ability to provide useful chemical start points. Target proteins without catalytic function, or those that may be difficult to express and purify, are amenable to these methods. Here, we provide a review of the applications of thermal unfolding methods applied in hit finding during early drug discovery.

In silico prediction of hERG blockers using machine learning and deep learning approaches.

The human ether-à-go-go-related gene (hERG) is associated with drug cardiotoxicity. If the hERG channel is blocked, it will lead to prolonged QT interval and cause sudden death in severe cases. Therefore, it is important to evaluate the hERG-blocking property of compounds in early drug discovery. In this study, a dataset containing 4556 compounds with IC50 values determined by patch clamp techniques on mammalian lineage cells was collected, and hERG blockers and non-blockers were distinguished according to 3 single-thresholds and 2 binary thresholds. Four machine learning (ML) algorithms combining four molecular fingerprints and molecular descriptors as well as graph convolutional neural networks (GCN) were used to construct a series of binary classification models. The results showed that the best models varied for different thresholds. The ML models implemented by support vector machine and random forest performed well based on Morgan fingerprints and molecular descriptors, with AUCs ranging from 0.884 to 0.950. GCN showed superior prediction performance with AUCs above 0.952, which might be related to its direct extraction of molecular features from the original input. Meanwhile, the classification of binary threshold was better than that of single threshold, which could provide us with a more accurate prediction of hERG blockers. At last, the applicability domain for the model was defined, and seven structural alerts that might generate hERG blockage were identified by information gain and substructure frequency analysis. Our work would be beneficial for identifying hERG blockers in chemicals.

Binding Site Detection Remastered: Enabling Fast, Robust, and Reliable Binding Site Detection and Descriptor Calculation with DoGSite3

Binding site prediction on protein structures is a crucial step in early phase drug discovery whenever experimental or predicted structure models are involved. DoGSite belongs to the widely used tools for this task. It is a grid-based method that uses a Difference-of-Gaussian filter to detect cavities on the protein surface. We recently reimplemented the first version of this method, released in 2010, focusing on improved binding site detection in the presence of ligands and optimized parameters for more robust, reliable, and fast predictions and binding site descriptor calculations. Here, we introduce the new version, DoGSite3, compare it to its predecessor, and re-evaluate DoGSite on published data sets for a large-scale comparative performance evaluation.

Abstract LB113: Bio-printed 3D cell models and high-content imaging for testing anti-cancer compounds

Abstract The discovery and screening of anti-cancer drugs is a vast field that is constantly evolving. More advanced cell-based models are needed to have an efficient way of studying cellular and subcellular effects of compound treatments, especially in early drug discovery. 3D cellular models that more accurately represent various microenvironments are very important for accurate drug screening and disease modeling. However, the complexity of those models is a limiting factor for the wider adoption of 3D models for research and screening. To address this, we have developed an automated workflow that includes a multitool 3D printing/automation robot enabling printing cells in a hydrogel-based matrix and high-content imaging for the characterization of phenotypic effects of compounds. We demonstrate examples of several bio-printed 3D cell models that can be used for testing anti-cancer drugs. For the development of 3D cellular models, we used VitroGel/VitroInk, xeno-free (animal origin-free) bio-functional hydrogel matrices. Hydrogel matrices with cells were used for printing 3D cellular patterns in 96 well plates, or dispensing cells in hydrogel “domes”. We have utilized a variety of widely used cancer lines HCT-116, HELA, HepG2, and MCF-7, as well as patient-derived triple-negative breast cancer cell line 4IC. Cells mixed with hydrogels/inks were dispensed/printed into a 96-well format using the multi-tool robotic platform, BioAssemblyBot. The platform enabled efficient dispensing/printing of cells into domes, lines, or other patterns. This assay was used for compound testing and evaluation of the anti-cancer effects of various drugs. The integrated system included a high-content confocal imager (IXM-C) and enabled automated seeding, bioprinting, liquid handling, plate transferring, as well as high-content imaging. The 3D bioprinted cells were monitored daily using imaging in transmitted light. In 2-3 days, cells formed spheroids in the matrix. Those were treated with a panel of anti-cancer drugs including doxorubicin, cytarabine, taxol, mitomycin, romidepsin, cisplatin, trametinib, and other compounds with various modes of action. Cells were treated with compounds for 72h, then stained and imaged for the endpoint measurements. 3D models were stained with viability dyes including Calcein AM, EthD, and Hoechst stain to evaluate numbers of live, dead, and total cells, then imaged using the IXM-C confocal imaging system. Image analysis allowed the characterization of cytostatic and cytotoxic effects of various compounds measuring the impact of compounds on cell proliferation, number of spheroids, spheroid size, cell death, and apoptosis. Different measurements, including spheroid size, and numbers of live and dead cells were used to determine EC50s for different compounds and cell types. The results showed the feasibility of 3D cellular models bioprinted with ECM matrices for anti-cancer drug screening workflows. An increase in throughput and ease of operation was achieved through automation. Also, imaging and data analysis methods provided valuable information about complex compound effects in 3D printed and cell-tissue-engineered cancer models. Citation Format: Prathyushakrishna Macha, John Huang, Zhisong Tong, James B. Hoying, Oksana Sirenko. Bio-printed 3D cell models and high-content imaging for testing anti-cancer compounds [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB113.

Abstract 2759: Real-time cellular target engagement and protein quantification for drug discovery

Abstract Preclinical assessment of direct drug-target engagement and analysis of binding kinetics within the cellular environment is essential for development of safer and more effective therapeutics. Infusion of such critical data into early drug discovery would significantly reduce the failure rate of new drug candidates, accelerate drug discovery, and validate repurposing of existing drugs. Described here is a novel drug-target engagement technology that can sensitively interrogate direct binding of drugs to cellular, bacterial, or viral protein targets within the physiological environment. Micro-Tag cell target engagement technology is based on complementation of a small 15-amino acid subunit with a large subunit into an active RNA-processing enzyme. The small subunit can be cloned to any drug target for transient or stable expression using existing tools such as CRISPR. Upon complementation, the active enzyme cleaves a FRET-based oligonucleotide substrate resulting in rapid generation of fluorescent signal that can be quantified in real time. The Micro-Tag technology enables in-cell quantitation of drug target levels using qPCR systems. This is the next-generation of target engagement technology that allows for real-time monitoring of drug-target interaction in the cell. We show here data demonstrating direct engagement of several reference compounds and novel small molecules with high-profile cancer targets such as K-RAS, MTH1, EGFR, and UBE2N. Selectivity of these drugs to the targets in the cell is further delineated using their mutant counterparts as well as inactive stereoisomer compounds. The Micro-Tag cell target engagement technology provides the power of cell target engagement to a large family of target proteins. It can be employed for high-throughput screens directed at initial on-target ranking or med-chem optimization of drug candidates. It is amenable for interrogation of drug candidates across various modalities: small molecules, peptides, antibodies and PROTACs. Importantly, this next-generation target engagement technology seamlessly integrates with qPCR systems, fluorescence microscopy, live-cell microscopy, FACS analysis, and offers multiplexing capability, thus allowing for further mechanistic insight. Citation Format: Ivan Babic, Nikolas Bryan, Claire Cunningham, Avery Sampson, Daniel Starczynowski, Elmar Nurmemmedov. Real-time cellular target engagement and protein quantification for drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2759.

Leveraging Advanced In Silico Techniques in Early Drug Discovery: A Study of Potent Small-Molecule YAP-TEAD PPI Disruptors.

Disruption of the YAP-TEAD protein-protein interaction is an attractive therapeutic strategy in oncology to suppress tumor progression and cancer metastasis. YAP binds to TEAD at a large flat binding interface (∼3500 Å2) devoid of a well-defined druggable pocket, so it has been difficult to design low-molecular-weight compounds to abrogate this protein-protein interaction directly. Recently, work by Furet and coworkers (ChemMedChem 2022, DOI: 10.1002/cmdc.202200303) reported the discovery of the first class of small molecules able to efficiently disrupt the transcriptional activity of TEAD by binding to a specific interaction site of the YAP-TEAD binding interface. Using high-throughput in silico docking, they identified a virtual screening hit from a hot spot derived from their previously rationally designed peptidic inhibitor. Structure-based drug design efforts led to the optimization of the hit compound into a potent lead candidate. Given advances in rapid high-throughput screening and rational approaches to peptidic ligand discovery for challenging targets, we analyzed the pharmacophore features involved in transferring from the peptidic to small-molecule inhibitor that could enable small-molecule discovery for such targets. Here, we show retrospectively that pharmacophore analysis augmented by solvation analysis of molecular dynamics trajectories can guide the designs, while binding free energy calculations provide greater insight into the binding conformation and energetics accompanying the association event. The computed binding free energy estimates agree well with experimental findings and offer useful insight into structural determinants that influence ligand binding to the TEAD interaction surface, even for such a shallow binding site. Taken together, our results demonstrates the utility of advanced in silico methods in structure-based design efforts for difficult-to-drug targets such as the YAP-TEAD transcription factor complex.

A pharmacophore-based approach to demonstrating the scope of alcohol dehydrogenases

Barriers to the ready adoption of biocatalysis into asymmetric synthesis for early stage medicinal chemistry are addressed, using ketone reduction by alcohol dehydrogenase as a model reaction. An efficient substrate screening approach is used to show the wide substrate scope of commercial alcohol dehydrogenase enzymes, with a high tolerance to chemical groups employed in drug discovery (heterocycle, trifluoromethyl and nitrile/nitro groups) observed. We use our screening data to build a preliminary predictive pharmacophore-based screening tool using Forge software, with a precision of 0.67/1, demonstrating the potential for developing substrate screening tools for commercially available enzymes without publicly available structures. We hope that this work will facilitate a culture shift towards adopting biocatalysis alongside traditional chemical catalytic methods in early stage drug discovery.

High throughput screening of 0.5 Million compounds against CRAF using Alpha CETSAⓇ.

The cellular thermal shift assay (CETSA®) has increasingly been used in early drug discovery to provide a measure of cellular target engagement. Traditionally, CETSA has been employed for bespoke questions with small to medium throughput and has predominantly been applied during hit validation rather than in hit identification. Using a CETSA screen versus the kinase CRAF, we assessed 3 key questions: (1) technical feasibility - could the CETSA methodology technically be applied at truly high throughput scale? (2) relevance - could hits suitable for further optimisation be identified? (3) reliability - would the approach identify known chemical equity. Here, we describe the first large scale AlphaLISA SureFire based CETSA (Alpha CETSA) approach allowing us to screen a large library of almost 0.5 million compounds. We discuss the issues overcome in automating and executing the screen and describe the resulting screen output.

Repositioning Cannabinoids and Terpenes as Novel EGFR-TKIs Candidates for Targeted Therapy Against Cancer: A virtual screening model using CADD and biophysical simulations

This study examines the potential of Cannabis sativa L. plants to be repurposed as therapeutic agents for cancer treatment through designing of hybrid Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). A set of 50 phytochemicals was taken from Cannabinoids and Terpenes and subjected for screening using Semi-flexible and Flexible Molecular Docking methods, MM-GBSA free binding energy computations, and pharmacokinetic/pharmacodynamic (ADME-Tox) predictions. Nine promising phytochemicals, Cannabidiolic acid (CBDA), Cannabidiol (CBD), Tetrahydrocannabivarin (THCV), Dronabinol (Δ-9-THC), Delta-8-Tetrahydrocannabinol (Δ-8-THC), Cannabicyclol (CBL), Delta9-tetrahydrocannabinolic acid (THCA), Beta-Caryophyllene (BCP), and Gamma-Elemene (γ-Ele) were identified as potential EGFR-TKIs natural product candidates for cancer therapy. To further validate these findings, a set of Molecular Dynamics simulations were conducted over a 200 ns trajectory. This hybrid early drug discovery screening strategy has the potential to yield a new generation of EGFR-TKIs based on natural cannabis products, suitable for cancer therapy. In addition, the application of this computational strategy in the virtual screening of both natural and synthetic chemical libraries could support the discovery of a wide range of lead drug agents to address numerous diseases.

A Symbolic Regression Model for the Prediction of Drug Binding to Human Liver Microsomes

It is common practice in the early drug discovery process to conduct in vitro screening experiments using liver microsomes in order to obtain an initial assessment of test compound metabolic stability. Compounds which bind to liver microsomes are unavailable for interaction with the drug metabolizing enzymes. As such, assessment of the unbound fraction of compound available for biotransformation is an important factor for interpretation of in vitro experimental results and to improve prediction of the in vivo metabolic clearance. Various in silico methods have been proposed for the prediction of test compound binding to microsomes, from various simple lipophilicity-based models with moderate performance to sophisticated machine learning models which demonstrate superior performance at the cost of increased complexity and higher data requirements. In this work, we attempt to strike a middle ground by developing easily implementable equations with improved predictive performance. We employ a symbolic regression approach based on a medium-size in-house data set of fraction unbound in human liver microsomes measurements allowing the identification of novel equations with improved performance. We validate the model performance on an in-house held-out test set and an external validation set.

Benchmarking In Silico Tools for Cysteine pKa Prediction.

Accurate estimation of the pKa's of cysteine residues in proteins could inform targeted approaches in hit discovery. The pKa of a targetable cysteine residue in a disease-related protein is an important physiochemical parameter in covalent drug discovery, as it influences the fraction of nucleophilic thiolate amenable to chemical protein modification. Traditional structure-based in silico tools are limited in their predictive accuracy of cysteine pKa's relative to other titratable residues. Additionally, there are limited comprehensive benchmark assessments for cysteine pKa predictive tools. This raises the need for extensive assessment and evaluation of methods for cysteine pKa prediction. Here, we report the performance of several computational pKa methods, including single-structure and ensemble-based approaches, on a diverse test set of experimental cysteine pKa's retrieved from the PKAD database. The dataset consisted of 16 wildtype and 10 mutant proteins with experimentally measured cysteine pKa values. Our results highlight that these methods are varied in their overall predictive accuracies. Among the test set of wildtype proteins evaluated, the best method (MOE) yielded a mean absolute error of 2.3 pK units, highlighting the need for improvement of existing pKa methods for accurate cysteine pKa estimation. Given the limited accuracy of these methods, further development is needed before these approaches can be routinely employed to drive design decisions in early drug discovery efforts.

Ringtail: A Python Tool for Efficient Management and Storage of Virtual Screening Results.

Virtual screening using molecular docking is now routinely used for the rapid evaluation of very large ligand libraries in early stage drug discovery. As the size of compound libraries which can feasibly be screened grows, so do the challenges in result management and storage. Here we introduce Ringtail, a new Python tool in the AutoDock Suite for efficient storage and analysis of virtual screening data based on portable SQLite databases. Ringtail is designed to work with AutoDock-GPU and AutoDock Vina out-of-the-box. Its modular design also allows for easy extension to support input file types from other docking software, different storage solutions, and incorporation into other applications. Ringtail's SQLite database output can dramatically reduce the required disk storage (36-46 fold) by selecting individual poses to store and by taking advantage of the relational database format. Filtering times are also dramatically reduced, requiring minutes to filter millions of ligands. Thus, Ringtail is a tool that can immediately integrate into existing virtual screening pipelines using AutoDock-GPU and Vina, and is scriptable and modifiable to fit specific user needs.

Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods

Chemical mutagenicity is a serious issue that needs to be addressed in early drug discovery. Over a long period of time, medicinal chemists have manually summarized a series of empirical rules for the optimization of chemical mutagenicity. However, given the rising amount of data, it is getting more difficult for medicinal chemists to identify more comprehensive chemical rules behind the biochemical data. Herein, we integrated a large Ames mutagenicity data set with 8576 compounds to derive mutagenicity transformation rules for reversing Ames mutagenicity via matched molecular pairs analysis. A well-trained consensus model with a reasonable applicability domain was constructed, which showed favorable performance in the external validation set with an accuracy of 0.815. The model was used to assess the generalizability and validity of these mutagenicity transformation rules. The results demonstrated that these rules were of great value and could provide inspiration for the structural modifications of compounds with potential mutagenic effects. We also found that the local chemical environment of the attachment points of rules was critical for successful transformation. To facilitate the use of these mutagenicity transformation rules, we integrated them into ADMETopt2 (http://lmmd.ecust.edu.cn/admetsar2/admetopt2/), a free web server for optimization of chemical ADMET properties. The above-mentioned approach would be extended to the optimization of other toxicity endpoints.Graphical

In silico modeling revealed phytomolecules derived from Cymbopogon citratus (DC.) leaf extract as promising candidates for malaria therapy

The emergence of varying levels of resistance to currently available antimalarial drugs significantly threatens global health. This factor heightens the urgency to explore bioactive compounds from natural products with a view to discovering and developing newer antimalarial drugs with novel mode of actions. Therefore, we evaluated the inhibitory effects of sixteen phytocompounds from Cymbopogon citratus leaf extract against Plasmodium falciparum drug targets such as P. falciparum circumsporozoite protein (PfCSP), P. falciparum merozoite surface protein 1 (PfMSP1) and P. falciparum erythrocyte membrane protein 1 (PfEMP1). In silico approaches including molecular docking, pharmacophore modeling and 3D-QSAR were adopted to analyze the inhibitory activity of the compounds under consideration. The molecular docking results indicated that a compound swertiajaponin from C. citratus exhibited a higher binding affinity (−7.8 kcal/mol) to PfMSP1 as against the standard artesunate-amodiaquine (−6.6 kcal/mol). Swertiajaponin also formed strong hydrogen bond interactions with LYS29, CYS30, TYR34, ASN52, GLY55 and CYS28 amino acid residues. In addition, quercetin another compound from C. citratus exhibited significant binding energies −6.8 and −8.3 kcal/mol with PfCSP and PfEMP1, respectively but slightly lower than the standard artemether-lumefantrine with binding energies of −7.4 kcal/mol against PfCSP and −8.7 kcal/mol against PfEMP1. Overall, the present study provides evidence that swertiajaponin and other phytomolecules from C. citratus have modulatory properties toward P. falciparum drug targets and thus may warrant further exploration in early drug discovery efforts against malaria. Furthermore, these findings lend credence to the folkloric use of C. citratus for malaria treatment. Communicated by Ramaswamy H. Sarma

Trio-pharmacophore DNA-encoded chemical library for simultaneous selection of fragments and linkers

The split-and-pool method has been widely used to synthesize chemical libraries of a large size for early drug discovery, albeit without the possibility of meaningful quality control. In contrast, a self-assembled DNA-encoded chemical library (DEL) allows us to construct an m x n-member library by mixing an m-member and an n-member pre-purified sub-library. Herein, we report a trio-pharmacophore DEL (T-DEL) of m x l x n members through assembling three pre-purified and validated sub-libraries. The middle sub-library is synthesized using DNA-templated synthesis with different reaction mechanisms and designed as a linkage connecting the fragments displayed on the flanking two sub-libraries. Despite assembling three fragments, the resulting compounds do not exceed the up-to-date standard of molecular weight regarding drug-likeness. We demonstrate the utility of T-DEL in linker optimization for known binding fragments against trypsin and carbonic anhydrase II and by de novo selections against matrix metalloprotease-2 and −9.

Selection of Optimal Cell Lines for High-Content Phenotypic Screening

High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity ("phenoactivity") and grouping compounds with similar MOA by similar phenotype ("phenosimilarity"). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g., detecting phenoactivity vs inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and a set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.

Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design.

Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment of CDK4/6 inhibitors. Generative models that take advantage of deep learning are being increasingly integrated into early drug discovery for hit identification and lead optimization. Here we report the discovery of a highly potent and selective macrocyclic CDK2 inhibitor QR-6401 (23) accelerated by the application of generative models and structure-based drug design (SBDD). QR-6401 (23) demonstrated robust antitumor efficacy in an OVCAR3 ovarian cancer xenograft model via oral administration.

Perspective on High-Throughput Bioanalysis to Support In Vitro Assays in Early Drug Discovery

As the desire for a shortened design/make/test/learn cycle increases in early drug discovery, the pressure to rapidly deliver drug metabolism pharmacokinetic data continues to rise. From a bioanalytical standpoint, in vitro assays are challenging because they are amenable to automation and thus capable of generating a high number of samples for analysis. To keep up with analysis demands, automated method development workflows, rapid sample analysis approaches and efficient data analysis software must be utilized. This work provides an outline of how we implemented those three aspects to provide bioanalytical support for in vitro drug metabolism pharmacokinetic assays, which include developing hundreds of mass spectrometry methods and analyzing thousands of samples per week, while delivering a median bioanalytical turnaround time of 1-2 business days.

Pose classification using three-dimensional atomic structure-based neural networks applied to ion channel-ligand docking.

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.

Antimony salt-promoted cyclization facilitating on-DNA syntheses of dihydroquinazolinone derivatives and its applications

DNA-encoded chemical libraries technology has become a novel approach to finding hit compounds in early drug discovery. The chemical space in a DEL would be expanded to realize its full potential, especially when integrating privileged scaffold dihydroquinazoline that has demonstrated a variety of diverse bioactivities. Driven by the requirement of parallel combinatorial synthesis, we here report a facile synthesis of on-DNA dihydroquinazolinone from aldehyde and anthranilamide. This DNA-compatible reaction was promoted by antimony trichloride, which has been proven to accelerate the reaction and improve conversions. Notably, the broad substrate scope of aldehydes and anthranilamides was explored under the mild reaction condition to achieve moderate-to-excellent conversion yields. We further applied the reaction into on-DNA macrocyclization, obtaining macrocycles embedded dihydroquinazolinone scaffold in synthetically useful conversion yields.