Multidimensional De Novo Design Reveals 5‐HT2B Receptor‐Selective Ligands

We report a multi‐objective de novo design study driven by synthetic tractability and aimed at the prioritization of computer‐generated 5‐HT_2B receptor ligands with accurately predicted target‐binding affinities. Relying on quantitative bioactivity models we designed and synthesized structurally novel, selective, nanomolar, and ligand‐efficient 5‐HT_2B modulators with sustained cell‐based effects. Our results suggest that seamless amalgamation of computational activity prediction and molecular design with microfluidics‐assisted synthesis enables the swift generation of small molecules with the desired polypharmacology.     

From Complex Natural Products to Simple Synthetic Mimetics by Computational De Novo Design

We present the computational de novo design of synthetically accessible chemical entities that mimic the complex sesquiterpene natural product (?)‐Englerin A. We synthesized lead‐like probes from commercially available building blocks and profiled them for activity against a computationally predicted panel of macromolecular targets. Both the design template (?)‐Englerin A and its low‐molecular weight mimetics presented nanomolar binding affinities and antagonized the transient receptor potential calcium channel TRPM8 in a cell‐based assay, without showing target promiscuity or frequent‐hitter properties. This proof‐of‐concept study outlines an expeditious solution to obtaining natural‐product‐inspired chemical matter with desirable properties.     

Fragment‐Based De Novo Design Reveals a Small‐Molecule Inhibitor of Helicobacter Pylori HtrA

Sustained identification of innovative chemical entities is key for the success of chemical biology and drug discovery. We report the fragment‐based, computer‐assisted de novo design of a small molecule inhibiting Helicobacter pylori HtrA protease. Molecular binding of the designed compound to HtrA was confirmed through biophysical methods, supporting its functional activity in vitro. Hit expansion led to the identification of the currently best‐in‐class HtrA inhibitor. The results obtained reinforce the validity of ligand‐based de novo design and binding‐kinetics‐guided optimization for the efficient discovery of pioneering lead structures and prototyping drug‐like chemical probes with tailored bioactivity.     

Deorphaning Pyrrolopyrazines as Potent Multi‐Target Antimalarial Agents

The discovery of pyrrolopyrazines as potent antimalarial agents is presented, with the most effective compounds exhibiting EC₅₀ values in the low nanomolar range against asexual blood stages of Plasmodium falciparum in human red blood cells, and Plasmodium berghei liver schizonts, with negligible HepG2 cytotoxicity. Their potential mode of action is uncovered by predicting macromolecular targets through avant‐garde computer modeling. The consensus prediction method suggested a functional resemblance between ligand binding sites in non‐homologous target proteins, linking the observed parasite elimination to IspD, an enzyme from the non‐mevalonate pathway of isoprenoid biosynthesis, and multi‐kinase inhibition. Further computational analysis suggested essential P. falciparum kinases as likely targets of our lead compound. The results obtained validate our methodology for ligand‐ and structure‐based target prediction, expand the bioinformatics toolbox for proteome mining, and provide unique access to deciphering polypharmacological effects of bioactive chemical agents.     

De Novo Design,Synthesis and Characterisation of MP3, A New Catalytic Four‐Helix Bundle Hemeprotein

A new artificial metalloenzyme, MP3 (MiniPeroxidase 3), designed by combining the excellent structural properties of four‐helix bundle protein scaffolds with the activity of natural peroxidases, was synthesised and characterised. This new hemeprotein model was developed by covalently linking the deuteroporphyrin to two peptide chains of different compositions to obtain an asymmetric helix–loop–helix/heme/helix–loop–helix sandwich arrangement, characterised by 1) a His residue on one chain that acts as an axial ligand to the iron ion; 2) a vacant distal site that is able to accommodate exogenous ligands or substrates; and 3) an Arg residue in the distal site that should assist in hydrogen peroxide activation to give an HRP‐like catalytic process. MP3 was synthesised and characterised as its iron complex. CD measurements revealed the high helix‐forming propensity of the peptide, confirming the appropriateness of the model procedure; UV/Vis, MCD and EPR experiments gave insights into the coordination geometry and the spin state of the metal. Kinetic experiments showed that Fe^III–MP3 possesses peroxidase‐like activity comparable to R38A–hHRP, highlighting the possibility of mimicking the functional features of natural enzymes. The synergistic application of de novo design methods, synthetic procedures, and spectroscopic characterisation, described herein, demonstrates a method by which to implement and optimise catalytic activity for an enzyme mimetic.     

Design of New Ligands for the Palladium‐Catalyzed Arylation of α‐Branched Secondary Amines

In Pd‐catalyzed C? N cross‐coupling reactions, α‐branched secondary amines are difficult coupling partners and the desired products are often produced in low yields. In order to provide a robust method for accessing N‐aryl α‐branched tertiary amines, new catalysts have been designed to suppress undesired side reactions often encountered when these amine nucleophiles are used. These advances enabled the arylation of a wide array of sterically encumbered amines, highlighting the importance of rational ligand design in facilitating challenging Pd‐catalyzed cross‐coupling reactions.     

Comb Poly(Oligo(2‐Ethyl‐2‐Oxazoline)Methacrylate)‐Peptide Conjugates Prepared by Aqueous Cu(0)‐Mediated Polymerization and Reductive Amination

The controlled synthesis of poly(oligo(2‐ethyl‐2‐oxazoline)methacrylate) (P(OEtOxMA)) polymers by Cu(0)‐mediated polymerization in water/methanol mixtures is reported. Utilizing an acetal protected aldehyde initiator for the polymerization, well‐defined polymers are synthesized (>99% conversion, Ð < 1.25) with subsequent postpolymerization deprotection resulting in α‐aldehyde end group containing comb polymers. These P(OEtOxMA) are subsequently site‐specifically conjugated, via reductive amination, to a dipeptide (NH₂‐Gly‐Tyr‐COOH) as a model peptide, prior to conjugation to the functional peptide oxytocin. The resulting oxytocin conjugates are evaluated in comparison to poly(oligo(ethylene glycol) methyl ether methacrylate) combs synthesized in the same manner for potential effects on thermal stability in comparison to the native peptide.

     

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.     

Fragment‐Based Drug Design Facilitated by Protein‐Templated Click Chemistry: Fragment Linking and Optimization of Inhibitors of the Aspartic Protease Endothiapepsin

There is an urgent need for the development of efficient methodologies that accelerate drug discovery. We demonstrate that the strategic combination of fragment linking/optimization and protein‐templated click chemistry is an efficient and powerful method that accelerates the hit‐identification process for the aspartic protease endothiapepsin. The best binder, which inhibits endothiapepsin with an IC₅₀ value of 43 μm , represents the first example of triazole‐based inhibitors of endothiapepsin. Our strategy could find application on a whole range of drug targets.     

Topotecan‐Loaded Mesoporous Silica Nanoparticles for Reversing Multi‐Drug Resistance by Synergetic Chemoradiotherapy

Multi‐drug resistance (MDR) has become a major challenge for the further improvement of chemotherapy. Thus, more effective strategies for further enhancing the treatment against cancer by overcoming MDR are warranted. In this study, by the encapsulation of the radiosensitizing drug TPT into mesoporous silica nanoparticles (MSNs), the combined use of drug‐delivered chemotherapy and high‐energy X‐ray induced radiotherapy could produce synergetic chemoradiotherapeutic effects to kill multi‐drug resistant cells through significant DNA damage, thus leading to an efficient circumvention of MDR. We hope that this synergetic dual‐mode treatment strategy may achieve higher oncolytic efficacy and find use in future clinical anti‐MDR applications.     

ZnO‐Functionalized Upconverting Nanotheranostic Agent: Multi‐Modality Imaging‐Guided Chemotherapy with On‐Demand Drug Release Triggered by pH

Limited therapeutic efficiency and severe side effects in patients are two major issues existing in current chemotherapy of cancers in clinic. To design a proper theranostic platform seems thus quite needed to target cancer cells accurately by bioimaging and simultaneously release drugs on demand without premature leakage. A novel ZnO‐functionalized upconverting nanotheranostic platform has been fabricated for clear multi‐modality bioimaging (upconversion luminescence (UCL), computed tomography (CT), and magnetic resonance imaging (MRI)) and specific pH‐triggered on‐demand drug release. In our theranostic platform multi‐modality imaging provides much more detailed and exact information for cancer diagnosis than single‐modality imaging. In addition, ZnO can play the role of a “gatekeeper” to efficiently block the drug in the mesopores of the as‐prepared agents until it is dissolved in the acidic environment around tumors to realize sustained release of the drug. More importantly, the biodegradable ZnO, which is non‐toxic against normal tissues, endows the as‐prepared agents with high therapeutic effectiveness but very low side effects. These findings are of great interests and will inspire us much to develop novel effective imaging‐guided on‐demand chemotherapies in cancer treatment.     

Fragment Linking and Optimization of Inhibitors of the Aspartic Protease Endothiapepsin: Fragment‐Based Drug Design Facilitated by Dynamic Combinatorial Chemistry

Fragment‐based drug design (FBDD) affords active compounds for biological targets. While there are numerous reports on FBDD by fragment growing/optimization, fragment linking has rarely been reported. Dynamic combinatorial chemistry (DCC) has become a powerful hit‐identification strategy for biological targets. We report the synergistic combination of fragment linking and DCC to identify inhibitors of the aspartic protease endothiapepsin. Based on X‐ray crystal structures of endothiapepsin in complex with fragments, we designed a library of bis‐acylhydrazones and used DCC to identify potent inhibitors. The most potent inhibitor exhibits an IC₅₀ value of 54 nm , which represents a 240‐fold improvement in potency compared to the parent hits. Subsequent X‐ray crystallography validated the predicted binding mode, thus demonstrating the efficiency of the combination of fragment linking and DCC as a hit‐identification strategy. This approach could be applied to a range of biological targets, and holds the potential to facilitate hit‐to‐lead optimization.     

Design and Synthesis of New Biprivileged Molecular Scaffolds: Indolo‐Fused Benzodiazepinyl/quinoxalinyl benzimidazoles

The present article describes the design and synthesis of new biprivileged molecular scaffolds with diverse structural features. Commercially available, simple heterocyclic building blocks such as 4‐fluoro‐3‐nitrobenzoic acid, 2‐chloro‐3‐nitrobenzoic acid, and indoline were utilized for the synthesis of the novel heterocycles. Pictet–Spengler‐type condensation was used as a key step to construct tetracyclic indolo‐benzodiazepines and indolo‐quinoxalines linked with substituted benzimidazoles. Analysis of single crystals of representative compounds showed that these molecular skeletons have the potential to present various substituents with distinct three‐dimensional orientations.