Virtual combinatorial libraries: dynamic generation of molecular and supramolecular diversity by self-assembly

Development of Receptor for Advanced Glycation End Products (RAGE) ligands through target directed dynamic combinatorial chemistry: a novel class of possible antagonists

RAGE is a transmembrane receptor of immunoglobulin family that can bind various endogenous and exogenous ligands, initiating the inflammatory downstream signaling pathways, including inflammaging. Therefore, RAGE represents an attractive drug target for age-related diseases. For the development of small-molecule RAGE antagonists, we employed protein-templated dynamic combinatorial chemistry (ptDCC) using RAGE's VC1 domain as a template, the first application of this approach in the context of RAGE. The affinities of DCC hits were validated using microscale thermophoresis. Subsequent screening against AGE2 (glyceraldehyde-modified AGE)-sRAGE (solubleRAGE) (AGE2-BSA/sRAGE) interaction using ELISA tests led to the identification of antagonists with micromolar potency. Our findings not only demonstrate the successful application of ptDCC on RAGE but also highlight its potential to address the pressing need for alternative strategies for the development of small-molecule RAGE antagonists, an area of research that has experienced a slowdown in recent years.

Triple Adaptation of Constitutional Dynamic Networks of Imines in Response to Micellar Agents: Internal Uptake-Interfacial Localization-Shape Transition

Understanding the behavior of complex chemical reaction networks and how environmental conditions can modulate their organization as well as the associated outcomes may take advantage of the design of related artificial systems. Microenvironments with defined boundaries are of particular interest for their unique properties and prebiotic significance. Dynamic covalent libraries (DCvLs) and their underlying constitutional dynamic networks (CDNs) have been shown to be appropriate for studying adaptation to several processes, including compartmentalization. However, microcompartments (e.g., micelles) provide specific environments for the selective protection from interfering reactions such as hydrolysis and an enhanced chemical promiscuity due to the interface, governing different processes of network modulation. Different interactions between the micelles and the library constituents lead to dynamic sensing, resulting in different expressions of the network through pattern generation. The constituents integrated into the micelles are protected from hydrolysis and hence preferentially expressed in the network composition at the cost of constitutionally linked members. In the present work, micellar integration was observed for two processes: internal uptake based on hydrophobic forces and interfacial localization relying on attractive electrostatic interactions. The latter drives a complex triple adaptation of the network with feedback on the shape of the self-assembled entity. Our results demonstrate how microcompartments can enforce the expression of constituents of CDNs by reducing the hydrolysis of uptaken members, unravelling processes that govern the response of reactions networks. Such studies open the way toward using DCvLs and CDNs to understand the emergence of complexity within reaction networks by their interactions with microenvironments.

STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks

With the wide application of deep learning in Drug Discovery, deep generative model has shown its advantages in drug molecular generation. Generative adversarial networks can be used to learn the internal structure of molecules, but the training process may be unstable, such as gradient disappearance and model collapse, which may lead to the generation of molecules that do not conform to chemical rules or a single style. In this paper, a novel method called STAGAN was proposed to solve the difficulty of model training, by adding a new gradient penalty term in the discriminator and designing a parallel layer of batch normalization used in generator. As an illustration of method, STAGAN generated higher valid and unique molecules than previous models in training datasets from QM9 and ZINC-250K. This indicates that the proposed method can effectively solve the instability problem in the model training process, and can provide more instructive guidance for the further study of molecular graph generation.

Supramolecular multivalency effects enhance imine formation in aqueous medium allowing for dynamic modification of enzymatic activity

Imine formation under physiological conditions represents a challenging reaction due to the strong propensity of aldimines to be hydrolyzed. Herein we disclose the remarkable effect of supramolecular multivalency on increasing imine stability. A family of reactive aldehydes was synthesized bearing supramolecularly-active sites within their structure. The imine formation activity for such aldehydes was evaluated and compared with model aldehydes. The reaction of the best-performing species - containing two carboxylate groups-with a set of amines showed a significant decrease in imine yields as the degree of supramolecular multivalency between sidechains decreased. The reversible conjugation of amino acid derivatives and small peptides was also assayed, with excellent selectivities for the imine formation at the Nα position even in substrates containing competing sites. Preliminary results on protein bioconjugation revealed that a model enzyme could be dynamically inhibited upon reaction with the aldehyde, with its native activity being recovered by displacing the imine bonds with a suitable chemical effector (i.e., acylhydrazide).

Sequence-selective duplex formation and template effect in recognition-encoded oligoanilines

A new family of duplex-forming recognition encoded oligomers, capable of sequence selective duplex formation and template directed synthesis, was developed. Monomers equipped with both amine and aldehyde groups were functionalized with 2-trifluoromethylphenol or phosphine oxide as H-bond recognition units. Duplex formation and assembly properties of homo- and hetero-oligomers were studied by 19F and 1H NMR experiments in chloroform. The designed backbone prevents the undesired 1,2-folding allowing sequence-selective duplex formation, and the stability of the antiparallel duplex is 3-fold higher than the parallel arrangement. Dynamic combinatorial chemistry was exploited for the templated synthesis of complementary oligomers, showing that an aniline dimer can template the formation of the complementary imine. The key role of the H-bond recognition confers to the system the ability to discriminate a mutated donor monomer incapable of H-bonding. Sequence selective duplex formation combined with the template effect makes this system an attractive target for further studies.

Linkers in fragment-based drug design: an overview of the literature

INTRODUCTION

In fragment-based drug design, fragment linking is a popular strategy where two fragments binding to different sub-pockets of a target are linked together. This attractive method remains challenging especially due to the design of ideal linkers.

AREAS COVERED

The authors review the types of linkers and chemical reactions commonly used to the synthesis of linkers, including those utilized in protein-templated fragment self-assembly, where fragments are directly linked in the presence of the protein. Finally, they detail computational workflows and software including generative models that have been developed for fragment linking.

EXPERT OPINION

The authors believe that fragment linking offers key advantages for compound design, particularly for the design of bivalent inhibitors linking two distinct pockets of the same or different subunits. On the other hand, more studies are needed to increase the potential of protein-templated approaches in FBDD. Important computational tools such as structure-based de novo software are emerging to select suitable linkers. Fragment linking will undoubtedly benefit from developments in computational approaches and machine learning models.

DNA-Encoded Libraries: Towards Harnessing their Full Power with Darwinian Evolution

DNA-encoded library (DEL) technologies are transforming the drug discovery process, enabling the identification of ligands at unprecedented speed and scale. DEL makes use of libraries that are orders of magnitude larger than traditional high-throughput screens. While a DNA tag alludes to a genotype-phenotype connection that is exploitable for molecular evolution, most of the work in the field is performed with libraries where the tag serves as an amplifiable barcode but does not allow "translation" into the synthetic product it is linked to. In this Review, we cover technologies that enable the "translation" of the genetic tag into synthetic molecules, both biochemically and chemically, and explore how it can be used to harness Darwinian evolutionary pressure.

Transient self-assembly of metal-organic complexes

Implementing transient processes in networks of dynamic molecules holds great promise for developing new functional behaviours. Here we report that trichloroacetic acid can be used to temporarily rearrange networks of dynamic imine-based metal complexes towards new equilibrium states, forcing them to express complexes otherwise unfavourable in their initial equilibrium states. Basic design principles were determined for the creation of such networks. Where a complex distribution of products was obtained in the initial equilibrium state of the system, the transient rearrangement temporarily yielded a simplified output, forcing a more structured distribution of products. Where a single complex was obtained in the initial equilibrium state of the system, the transient rearrangement temporarily modified the properties of this complex. By doing so, the mechanical properties of an helical macrocyclic complex could be temporarily altered by rearranging it into a [2]catenane.

Identification of specific carbonic anhydrase inhibitors via in situ click chemistry, phage-display and synthetic peptide libraries: comparison of the methods and structural study

The development of highly active and selective enzyme inhibitors is one of the priorities of medicinal chemistry. Typically, various high-throughput screening methods are used to find lead compounds from a large pool of synthetic compounds, and these are further elaborated and structurally refined to achieve the desired properties. In an effort to streamline this complex and laborious process, new selection strategies based on different principles have recently emerged as an alternative. Herein, we compare three such selection strategies with the aim of identifying potent and selective inhibitors of human carbonic anhydrase II. All three approaches, in situ click chemistry, phage-display libraries and synthetic peptide libraries, led to the identification of more potent inhibitors when compared to the parent compounds. In addition, one of the inhibitor-peptide conjugates identified from the phage libraries showed greater than 100-fold selectivity for the enzyme isoform used for the compound selection. In an effort to rationalize the binding properties of the conjugates, we performed detailed crystallographic and NMR structural analysis, which revealed the structural basis of the compound affinity towards the enzyme and led to the identification of a novel exosite that could be utilized in the development of isoform specific inhibitors.

Fragment-Based Dynamic Combinatorial Chemistry for Identification of Selective α-Glucosidase Inhibitors

Efforts to combine advantages of fragment-based drug design (FBDD) and dynamic combinatorial chemistry (DCC) for the development of selective α-glucosidase inhibitors were described. Starting from 5 rationally designed fragments, two iterative dynamic combinatorial libraries (DCLs) comprising 29 acylhydrazone products were generated and screened using α-glucosidase and α-amylase as the templates. The optimal ligand identified showed substantial α-glucosidase inhibition with high selectivity over α-amylase as well as low cytotoxicity. Furthermore, inhibition type and detailed ligand/enzyme binding interactions were elucidated by the binding kinetic study and docking simulation, respectively.

In Situ Inhibitor Synthesis and Screening by Fluorescence Polarization: An Efficient Approach for Accelerating Drug Discovery

Target-directed dynamic combinatorial chemistry has emerged as a useful tool for hit identification, but has not been widely used, in part due to challenges associated with analyses involving complex mixtures. We describe an operationally simple alternative: in situ inhibitor synthesis and screening (ISISS), which links high-throughput bioorthogonal synthesis with screening for target binding by fluorescence. We exemplify the ISISS method by showing how coupling screening for target binding by fluorescence polarization with the reaction of acyl-hydrazides and aldehydes led to the efficient discovery of a potent and novel acylhydrazone-based inhibitor of human prolyl hydroxylase 2 (PHD2), a target for anemia treatment, with equivalent in vivo potency to an approved medicine.

In Situ Inhibitor Synthesis and Screening by Fluorescence Polarization: An Efficient Approach for Accelerating Drug Discovery

Target-directed dynamic combinatorial chemistry has emerged as a useful tool for hit identification, but has not been widely used, in part due to challenges associated with analyses involving complex mixtures. We describe an operationally simple alternative: in situ inhibitor synthesis and screening (ISISS), which links high-throughput bioorthogonal synthesis with screening for target binding by fluorescence. We exemplify the ISISS method by showing how coupling screening for target binding by fluorescence polarization with the reaction of acyl-hydrazides and aldehydes led to the efficient discovery of a potent and novel acylhydrazone-based inhibitor of human prolyl hydroxylase 2 (PHD2), a target for anemia treatment, with equivalent in vivo potency to an approved medicine.

Development of urease inhibitors by fragment-based dynamic combinatorial chemistry

In this study, fragment-based dynamic combinatorial chemistry (DCC) was explored for the development of novel urease inhibitors. Based on a rationally designed fragment, two iteratively evolved dynamic combinatorial libraries (DCLs) were generated and screened in the presence of urease template. The best ligand identified revealed not only strong urease inhibition but also low cytotoxicity. Additionally, possible inhibitory mechanism was elucidated in the binding kinetic study and docking simulation.

Toward in Silico Modeling of Dynamic Combinatorial Libraries

Dynamic combinatorial libraries (DCLs) display adaptive behavior, enabled by the reversible generation of their molecular constituents from building blocks, in response to external effectors, e.g., protein receptors. So far, chemoinformatics has not yet been used for the design of DCLs-which comprise a radically different set of challenges compared to classical library design. Here, we propose a chemoinformatic model for theoretically assessing the composition of DCLs in the presence and the absence of an effector. An imine-based DCL in interaction with the effector human carbonic anhydrase II (CA II) served as a case study. Support vector regression models for the imine formation constants and imine-CA II binding were derived from, respectively, a set of 276 imines synthesized and experimentally studied in this work and 4350 inhibitors of CA II from ChEMBL. These models predict constants for all DCL constituents, to feed software assessing equilibrium concentrations. They are publicly available on the dedicated website. Models rationally selected two amines and two aldehydes predicted to yield stable imines with high affinity for CA II and provided a virtual illustration on how effector affinity regulates DCL members.

Recent advances in DNA-encoded dynamic libraries

The DNA-encoded chemical library (DEL) has emerged as a powerful technology platform in drug discovery and is also gaining momentum in academic research. The rapid development of DNA-/DEL-compatible chemistries has greatly expanded the chemical space accessible to DELs. DEL technology has been widely adopted in the pharmaceutical industry and a number of clinical drug candidates have been identified from DEL selections. Recent innovations have combined DELs with other legacy and emerging techniques. Among them, the DNA-encoded dynamic library (DEDL) introduces DNA encoding into the classic dynamic combinatorial libraries (DCLs) and also integrates the principle of fragment-based drug discovery (FBDD), making DEDL a novel approach with distinct features from static DELs. In this Review, we provide a summary of the recently developed DEDL methods and their applications. Future developments in DEDLs are expected to extend the application scope of DELs to complex biological systems with unique ligand-discovery capabilities.

Biocompatible Diselenide-Containing Protein Hydrogels with Effective Visible-Light-Initiated Self-Healing Properties

Smart hydrogels are typical functional soft materials, but their functional and mechanical properties are compromised upon micro- or macro-mechanical damage. In contrast, hydrogels with self-healing properties overcome this limitation. Herein, a dual dynamic bind, cross-linked, self-healing protein hydrogel is prepared, based on Schiff base bonds and diselenide bonds. The Schiff base bond is a typical dynamic covalent bond and the diselenide bond is an emerging dynamic covalent bond with a visible light response, which gives the resulting hydrogel a dual response in visible light and a desirable self-healing ability. The diselenide-containing protein hydrogels were biocompatible due to the fact that their main component was protein. In addition, the hydrogels loaded with glucose oxidase (GOx) could be transformed into sols in glucose solution due to the sensitive response of the diselenide bonds to the generated hydrogen peroxide (H2O2) by enzymatic catalysis. This work demonstrated a diselenide-containing protein hydrogel that could efficiently self-heal up to nearly 100% without compromising their mechanical properties under visible light at room temperature.

Constitutional Dynamic Inhibition/Activation of Carbonic Anhydrases

In this review we consider one important member of the metalloenzymes family, the carbonic anhydrase (CA), involved in the treatment of several common diseases. Different approaches have emerged to regulate the activity of CA, mostly acting on the inner catalytic active site or outer microenvironment of the enzyme, leading to inhibition or activation of CA. In recent years, gradually increased attention has focused on the adoption of constitutional dynamic chemistry (CDC) strategies for the screening and discovery of potent inhibitors or activators. The participation of reversible covalent bonds enabled the enzyme itself to select the optimal ligands obtained from diverse building blocks with comparatively higher degree of variety, resulting in the fittest recognition of enzyme ligands from complex dynamic systems. With the increasing implementation of CDC for enzyme targets, it shows great potential for drug discovery or CO₂ capture applications.

Strategies for discovering resistance-breaking, safe and sustainable commercial herbicides with novel modes of action and chemotypes

Farmers need to manage weeds to grow and harvest crops that are essential to our food and energy supply, and herbicides are the most important tool in the farmers' armory to combat weeds. There is now a crisis in agriculture that has been brought about by herbicides being rendered ineffective by resistant weeds or withdrawn from the market due to safety concerns. Efficacious herbicides with novel modes of action (MoAs) and chemotypes are urgently needed to control resistant weeds and satisfy public and regulators' stringent requirements for safe and sustainable products. This article explores the main strategies being deployed by academic and industrial institutions to discover the next generation of commercial herbicides: phenotypic and in vitro target based approaches. There are early signs that much needed innovation and herbicidal products with novel MoAs are on the horizon from start-ups and established agrochemical companies. © 2021 Society of Chemical Industry. © 2021 Society of Chemical Industry.

Chemical Evolution of Antivirals Against Enterovirus D68 through Protein-Templated Knoevenagel Reactions

The generation of bioactive molecules from inactive precursors is a crucial step in the chemical evolution of life, however, mechanistic insights into this aspect of abiogenesis are scarce. Here, we investigate the protein-catalyzed formation of antivirals by the 3C-protease of enterovirus D68. The enzyme induces aldol condensations yielding inhibitors with antiviral activity in cells. Kinetic and thermodynamic analyses reveal that the bioactivity emerges from a dynamic reaction system including inhibitor formation, alkylation of the protein target by the inhibitors, and competitive addition of non-protein nucleophiles to the inhibitors. The most active antivirals are slowly reversible inhibitors with elongated target residence times. The study reveals first examples for the chemical evolution of bio-actives through protein-catalyzed, non-enzymatic C-C couplings. The discovered mechanism works under physiological conditions and might constitute a native process of drug development.

Hit-optimization using target-directed dynamic combinatorial chemistry: development of inhibitors of the anti-infective target 1-deoxy-d-xylulose-5-phosphate synthase

Target-directed dynamic combinatorial chemistry (tdDCC) enables identification, as well as optimization of ligands for un(der)explored targets such as the anti-infective target 1-deoxy-d-xylulose-5-phosphate synthase (DXPS). We report the use of tdDCC to first identify and subsequently optimize binders/inhibitors of the anti-infective target DXPS. The initial hits were also optimized for their antibacterial activity against E. coli and M. tuberculosis during subsequent tdDCC runs. Using tdDCC, we were able to generate acylhydrazone-based inhibitors of DXPS. The tailored tdDCC runs also provided insights into the structure-activity relationship of this novel class of DXPS inhibitors. The competition tdDCC runs provided important information about the mode of inhibition of acylhydrazone-based inhibitors. This approach holds the potential to expedite the drug-discovery process and should be applicable to a range of biological targets.

Identification of selective LdDHFR inhibitors using quantum chemical and molecular modeling approach

Among the various known targets for the treatment of Leishmaniasis, dihydrofolate reductase (DHFR) is an essential target which plays an important role in the folate metabolic pathway. In the current study, pharmacoinformatics approaches including quantum chemistry methods, molecular docking and molecular dynamics simulations have been utilized to identify selective Leishmania donovani DHFR (LdDHFR) inhibitors. Initially, for the design of new LdDHFR inhibitors, a virtual combinatorial library was created by considering various head groups (scaffolds), linkers and tail groups. The scaffolds utilized in the library design were selected on the basis of their proton affinity (PA) estimated using quantum chemical methods, required to make a strong H-bond interaction with negatively charged LdDHFR active site. Later on, molecular docking-based virtual screening was performed to screen the designed library. Selectivity of the chosen hits toward the LdDHFR was established through re-docking in the human DHFR enzyme (HsDHFR). Best five hits were subjected to molecular dynamics (MD) simulations to validate their selectivity as well as stability in LdDHFR. Out of the five hits, four were found to be energetically more favorable and promising for selective binding toward LdDHFR in comparison to HsDHFR.Communicated by Ramaswamy H. Sarma.

Identification of a Heteroleptic Pd6L6L'6 Coordination Cage by Screening of a Virtual Combinatorial Library

The design of structurally defined heteroleptic coordination cages is a challenging task, and only few examples are known to date. Here we describe a selection approach that allowed the identification of a novel hexanuclear Pd cage containing two types of dipyridyl ligands. A virtual combinatorial library of [Pd_nL_2n](BF₄)_2n complexes was prepared by mixing six different dipyridyl ligands with substoichiometric amounts of [Pd(CH₃CN)₄](BF₄)₂. Analysis of the equilibrated reaction mixture revealed the preferential formation of a heteroleptic [Pd₆L₆L'₆](BF₄)₁₂ assembly. The complex was prepared on a preparative scale by a targeted synthesis, and its structure was elucidated by single-crystal X-ray diffraction. It features an unprecedented trigonal-antiprismatic cage structure with two triangular Pd₃L₃ macrocycles bridged by six L' ligands. A related but significantly larger [Pd₆L₆L'₆](BF₄)₁₂ cage was obtained by using metalloligands instead of organic dipyridyl ligands.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

Chemoinformatics-based enumeration of chemical libraries: a tutorial

Virtual compound libraries are increasingly being used in computer-assisted drug discovery applications and have led to numerous successful cases. This paper aims to examine the fundamental concepts of library design and describe how to enumerate virtual libraries using open source tools. To exemplify the enumeration of chemical libraries, we emphasize the use of pre-validated or reported reactions and accessible chemical reagents. This tutorial shows a step-by-step procedure for anyone interested in designing and building chemical libraries with or without chemoinformatics experience. The aim is to explore various methodologies proposed by synthetic organic chemists and explore affordable chemical space using open-access chemoinformatics tools. As part of the tutorial, we discuss three examples of design: a Diversity-Oriented-Synthesis library based on lactams, a bis-heterocyclic combinatorial library, and a set of target-oriented molecules: isoindolinone based compounds as potential acetylcholinesterase inhibitors. This manuscript also seeks to contribute to the critical task of teaching and learning chemoinformatics.

Template-guided selection of RNA ligands using imine-based dynamic combinatorial chemistry

This study establishes the applicability of imine-based dynamic combinatorial chemistry to discover non-covalent ligands for RNA targets. We elucidate properties underlying the reactivity of arylamines and demonstrate target-guided amplification of tight binders in an amiloride-based dynamic library.

One-Pot Parallel Synthesis of Unclosed Cryptands-Searching for Selective Anion Receptors via Static Combinatorial Chemistry Techniques

We present the synthesis of 17 macrocyclic compounds having the structure of so-called unclosed cryptands, acting as anion receptors. These compounds possess amide functions playing the role of hydrogen-bond-donating systems. We have synthesized the presented compounds both by standard methods (using batch conditions) and by static combinatorial chemistry methods, using tetrabutylammonium dihydrogen phosphate as a template, promoting the lariat arm postfunctionalization reaction.

Enzymatic Noncovalent Synthesis

Enzymatic reactions and noncovalent (i.e., supramolecular) interactions are two fundamental nongenetic attributes of life. Enzymatic noncovalent synthesis (ENS) refers to a process where enzymatic reactions control intermolecular noncovalent interactions for spatial organization of higher-order molecular assemblies that exhibit emergent properties and functions. Like enzymatic covalent synthesis (ECS), in which an enzyme catalyzes the formation of covalent bonds to generate individual molecules, ENS is a unifying theme for understanding the functions, morphologies, and locations of molecular ensembles in cellular environments. This review intends to provide a summary of the works of ENS within the past decade and emphasize ENS for functions. After comparing ECS and ENS, we describe a few representative examples where nature uses ENS, as a rule of life, to create the ensembles of biomacromolecules for emergent properties/functions in a myriad of cellular processes. Then, we focus on ENS of man-made (synthetic) molecules in cell-free conditions, classified by the types of enzymes. After that, we introduce the exploration of ENS of man-made molecules in the context of cells by discussing intercellular, peri/intracellular, and subcellular ENS for cell morphogenesis, molecular imaging, cancer therapy, and other applications. Finally, we provide a perspective on the promises of ENS for developing molecular assemblies/processes for functions. This review aims to be an updated introduction for researchers who are interested in exploring noncovalent synthesis for developing molecular science and technologies to address societal needs.

Supramolecular catalysis by recognition-encoded oligomers: discovery of a synthetic imine polymerase

All key chemical transformations in biology are catalysed by linear oligomers. Catalytic properties could be programmed into synthetic oligomers in the same way as they are programmed into proteins, and an example of the discovery of emergent catalytic properties in a synthetic oligomer is reported. Dynamic combinatorial chemistry experiments designed to study the templating of a recognition-encoded oligomer by the complementary sequence have uncovered an unexpected imine polymerase activity. Libraries of equilibrating imines were formed by coupling diamine linkers with monomer building blocks composed of dialdehydes functionalised with either a trifluoromethyl phenol (D) or phosphine oxide (A) H-bond recognition unit. However, addition of the AAA trimer to a mixture of the phenol dialdehyde and the diamine linker did not template the formation of the DDD oligo-imine. Instead, AAA was found to be a catalyst, leading to rapid formation of long oligomers of D. AAA catalysed a number of different imine formation reactions, but a complementary phenol recognition group on the aldehyde reaction partner is an essential requirement. Competitive inhibition by an unreactive phenol confirmed the role of H-bonding in substrate recognition. AAA accelerates the rate of imine formation in toluene by a factor of 20. The kinetic parameters for this enzyme-like catalysis are estimated as 1 × 10^-3 s^-1 for k _cat and the dissociation constant for substrate binding is 300 μM. The corresponding DDD trimer was found to catalyse oligomerisation the phosphine oxide dialdehyde with the diamine linker, suggesting an important role for the backbone in catalysis. This unexpected imine polymerase activity in a duplex-forming synthetic oligomer suggests that there are many interesting processes to be discovered in the chemistry of synthetic recognition-encoded oligomers that will parallel those found in natural biopolymers.

Selection of DNA-Encoded Dynamic Chemical Libraries for Direct Inhibitor Discovery

Dynamic combinatorial libraries (DCLs) is a powerful tool for ligand discovery in biomedical research; however, the application of DCLs has been hampered by their low diversity. Recently, the concept of DNA encoding has been employed in DCLs to create DNA-encoded dynamic libraries (DEDLs); however, all current DEDLs are limited to fragment identification, and a challenging process of fragment linking is required after selection. We report an anchor-directed DEDL approach that can identify full ligand structures from large-scale DEDLs. This method is also able to convert unbiased libraries into focused ones targeting specific protein classes. We demonstrated this method by selecting DEDLs against five proteins, and novel inhibitors were identified for all targets. Notably, several selective BD1/BD2 inhibitors were identified from the selections against bromodomain 4 (BRD4), an important anti-cancer drug target. This work may provide a broadly applicable method for inhibitor discovery.

PNA-Based Dynamic Combinatorial Libraries (PDCL) and screening of lectins

Selections from dynamic combinatorial libraries (DCL) benefit from the dynamic nature of the library that can change constitution upon addition of a selection pressure, such as ligands binding to a protein. This technology has been predominantly used with small molecules interacting with each other through reversible covalent interaction. However, application of this technology in biomedical research and drug discovery has been limited by the reversibility of covalent exchange and the analytical deconvolution of small molecule fragments. Here we report a supramolecular approach based on the use of a constant short PNA tag to direct the combinatorial pairing of fragment. This PNA tag yields fast exchange kinetics, while still delivering the benefits of cooperativity, and provides favourable properties for analytical deconvolution by MALDI. A selection from >6,000 assemblies of glycans (mono-, di-, tri-saccharides) targeting AFL, a lectin from pathogenic fungus, yielded a 95 nM assembly, nearly three orders of magnitude better in affinity than the corresponding glycan alone (41 µM).

Optimized Inhibitors of MDM2 via an Attempted Protein-Templated Reductive Amination

Innovative and efficient hit-identification techniques are required to accelerate drug discovery. Protein-templated fragment ligations represent a promising strategy in early drug discovery, enabling the target to assemble and select its binders from a pool of building blocks. Development of new protein-templated reactions to access a larger structural diversity and expansion of the variety of targets to demonstrate the scope of the technique are of prime interest for medicinal chemists. Herein, we present our attempts to use a protein-templated reductive amination to target protein-protein interactions (PPIs), a challenging class of drug targets. We address a flexible pocket, which is difficult to achieve by structure-based drug design. After careful analysis we did not find one of the possible products in the kinetic target-guided synthesis (KTGS) approach, however subsequent synthesis and biochemical evaluation of each library member demonstrated that all the obtained molecules inhibit MDM2. The most potent library member (Ki =0.095 μm) identified is almost as active as Nutlin-3, a potent inhibitor of the p53-MDM2 PPI.

Dynamic Covalent Polymers for Biomedical Applications

The rapid development of supramolecular polymer chemistry and constitutional dynamic chemistry over the last decades has made tremendous impact on the emergence of dynamic covalent polymers. These materials are formed through reversible covalent bonds, endowing them with adaptive and responsive features that have resulted in high interest throughout the community. Owing to their intriguing properties, such as self-healing, shape-memory effects, recyclability, degradability, stimuli-responsiveness, etc., the materials have found multiple uses in a wide range of areas. Of special interest is their increasing use for biomedical applications, and many examples have been demonstrated in recent years. These materials have thus been used for the recognition and sensing of biologically active compounds, for the modulation of enzyme activity, for gene delivery, and as materials for cell culture, delivery, and wound-dressing. In this review, some of these endeavors are discussed, highlighting the many advantages and unique properties of dynamic covalent polymers for use in biology and biomedicine.

On the conditions for mimicking natural selection in chemical systems

The emergence of natural selection, requiring that reproducing entities present variations that may be inherited and passed on, was arguably the most important breakthrough in the self-organization of life. In this Perspective, the assumptions governing biological reproduction are confronted with physico-chemical principles that control the evolution of material systems. In biology, the reproduction of living organisms is never considered to be reversible, whereas microscopic reversibility is an essential principle in the physical description of matter. Here, we show that this discrepancy places constraints on the possibility of finding kinetic processes in the chemical world that are equivalent to natural selection in the biological one. Chemical replicators can behave in a similar fashion to living entities, provided that the reproduction cycle proceeds in a unidirectional way. For this to be the case, kinetic barriers must hinder the reverse process. The system must, thus, be held far from equilibrium and fed with a non-degraded (low-entropy) form of energy. The ensuing constraints must be factored in when proposing scenarios that account for the origin of life at the molecular level.

Affinity purification of multifunctional oligomeric ligands synthesizedviacontrolled radical polymerization

Abiotic oligomeric ligands with a strong affinity for a target peptide sequence were isolated by affinity purification from a pool of 30-mer acrylic random ter-oligomers that were synthesizedviaa controlled radical polymerization process.

Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation

With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-autoregressive approach have been effective in generating molecular graphs quickly and efficiently but have suffered from low performance. In this paper, we present an improved learning method involving a graph variational autoencoder for efficient molecular graph generation in a non-autoregressive manner. We introduce three additional learning objectives and incorporate them into the training of the model: approximate graph matching, reinforcement learning, and auxiliary property prediction. We demonstrate the effectiveness of the proposed method by evaluating it for molecular graph generation tasks using QM9 and ZINC datasets. The model generates molecular graphs with high chemical validity and diversity compared with existing non-autoregressive methods. It can also conditionally generate molecular graphs satisfying various target conditions.

Insights into real-time chemical processes in a calcium sensor protein-directed dynamic library

Dynamic combinatorial chemistry (DCC) has proven its potential in drug discovery speeding the identification of modulators of biological targets. However, the exchange chemistries typically take place under specific reaction conditions, with limited tools capable of operating under physiological parameters. Here we report a catalyzed protein-directed DCC working at low temperatures that allows the calcium sensor NCS-1 to find the best ligands in situ. Ultrafast NMR identifies the reaction intermediates of the acylhydrazone exchange, tracing the molecular assemblies and getting a real-time insight into the essence of DCC processes at physiological pH. Additionally, NMR, X-ray crystallography and computational methods are employed to elucidate structural and mechanistic aspects of the molecular recognition event. The DCC approach leads us to the identification of a compound stabilizing the NCS-1/Ric8a complex and whose therapeutic potential is proven in a Drosophila model of disease with synaptic alterations.

Dynamic combinatorial chemistry (DCC) is instrumental in the discovery of ligands for pharmaceutical targets. Here, the authors adapted DCC to work at 4 degrees Celsius and used it to identify a ligand for Neuronal Calcium Sensor-1 that promotes NCS-1/Ric8a protein-protein interaction.

Protein-Templated Dynamic Combinatorial Chemistry: Brief Overview and Experimental Protocol

Dynamic combinatorial chemistry (DCC) is a powerful tool to identify bioactive compounds. This efficient technique allows the target to select its own binders and circumvents the need for synthesis and biochemical evaluation of all individual derivatives. An ever-increasing number of publications report the use of DCC on biologically relevant target proteins. This minireview complements previous reviews by focusing on the experimental protocol and giving detailed examples of essential steps and factors that need to be considered, such as protein stability, buffer composition and cosolvents.

Supramolecular Nanotube Reactors for Production of Imine Polymers with Controlled Conformation, Size, and Chirality

A series of supramolecular nanotubes with inner diameters of 1, 4, 9, 12, 16, and 29 nm are prepared from amino acid lipids. The hydrophobic channels of the nanotubes act as reactors for the formation of imine polymers by not only effectively encapsulating the benzaldehyde and diacetyleneamine precursors of the imine monomers but also markedly accelerating imine formation. The nanotube inner diameter determines whether the imine monomers self-assemble into nanoparticles, nanotapes, nanocoils, or twisted nanofibers in the channels. UV-induced polymerization of the diacetylene units in the imine nanostructures followed by decomposition of the nanotubes into molecular dispersions of the constituent amino acid lipids results in expulsion of the polymerized imine nanostructures with retained conformation. The isolated nanocoils and twisted nanofibers retain the helicity and circular dichroism induced by the nanotubes, which exhibits supramolecular chirality, even though the components of the imine monomers are achiral. These supramolecular nanotubes with tunable diameters and functionalizable surfaces can be expected to be useful for the production of polymers with controlled conformation, size, and chirality without the need for rational design or chemical modification of the monomers or optimization of the polymerization conditions.

Postpolymerization modification based on dynamic imine chemistry for the synthesis of functional polyacetylenes

This study established a postpolymerization modification method for the preparation of functional polyacetylenes based on dynamic imine chemistry.