Selection of Color-Changing and Intensity-Increasing Fluorogenic Probe as Protein-Specific Indicator Obtained via the 10BASEd-T

To obtain a molecular probe for specific protein detection, we have synthesized fluorogenic probe library of vastdiversity on bacteriophage T7 via the gp10 based-thioetherificaion (10BASEd-T). A remarkable color-changing and turning-on probewas selected from the library, and its physicochemical properties upon target-specific binding were obtained. Combination analysesof fluorescence emission titration, isothermal titration calorimetry (ITC), and quantitative saturation-transfer difference (STD) NMRmeasurements followed by in silico docking simulation, rationalized most plausible geometry of the ligand-protein interaction

T-Rex: Text-assisted Retrosynthesis Prediction

As a fundamental task in computational chemistry, retrosynthesis prediction aims to identify a set of reactants to synthesize a target molecule. Existing template-free approaches only consider the graph structures of the target molecule, which often cannot generalize well to rare reaction types and large molecules. Here, we propose T-Rex, a text-assisted retrosynthesis prediction approach that exploits pre-trained text language models, such as ChatGPT, to assist the generation of reactants. T-Rex first exploits ChatGPT to generate a description for the target molecule and rank candidate reaction centers based both the description and the molecular graph. It then re-ranks these candidates by querying the descriptions for each reactants and examines which group of reactants can best synthesize the target molecule. We observed that T-Rex substantially outperformed graph-based state-of-the-art approaches on two datasets, indicating the effectiveness of considering text information. We further found that T-Rex outperformed the variant that only use ChatGPT-based description without the re-ranking step, demonstrate how our framework outperformed a straightforward integration of ChatGPT and graph information. Collectively, we show that text generated by pre-trained language models can substantially improve retrosynthesis prediction, opening up new avenues for exploiting ChatGPT to advance computational chemistry. And the codes can be found at https://github.com/lauyikfung/T-Rex

Learning Graph Models for Retrosynthesis Prediction

Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building on this viewpoint, this paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. The model first predicts the set of graph edits transforming the target into incomplete molecules called synthons. Next, the model learns to expand synthons into complete molecules by attaching relevant leaving groups. This decomposition simplifies the architecture, making its predictions more interpretable, and also amenable to manual correction. Our model achieves a top-1 accuracy of $53.7\%$, outperforming previous template-free and semi-template-based methods

A Rapid Cloning Method Employing Orthogonal End Protection

We describe a novel in vitro cloning strategy that combines standard tools in molecular biology with a basic protecting group concept to create a versatile framework for the rapid and seamless assembly of modular DNA building blocks into functional open reading frames. Analogous to chemical synthesis strategies, our assembly design yields idempotent composite synthons amenable to iterative and recursive split-and-pool reaction cycles. As an example, we illustrate the simplicity, versatility and efficiency of the approach by constructing an open reading frame composed of tandem arrays of a human fibronectin type III (FNIII) domain and the von Willebrand Factor A2 domain (VWFA2), as well as chimeric (FNIII)n-VWFA2-(FNIII)n constructs. Although we primarily designed this strategy to accelerate assembly of repetitive constructs for single-molecule force spectroscopy, we anticipate that this approach is equally applicable to the reconstitution and modification of complex modular sequences including structural and functional analysis of multi-domain proteins, synthetic biology or the modular construction of episomal vectors

Approaches to the Total Synthesis of Puupehenone-Type Marine Natural Products

Puupehenones have been isolated from the marine sponge Chondrosia chucalla, which belong to a growing family of natural products with more than 100 members. These marine natural products have attracted increasing attention mainly due to their wide variety of biological activities such as antitumor, antiviral, and anti-HIV, and thus offer promising opportunities for new drug development. This chapter covers the approaches to the total synthesis of puupehenone-type marine natural products including puupehenol, puupehenone, puupehedione, and halopuupehenones. The routes begin with the construction of their basic skeletons, followed by the modification of their C- and D-rings. The contents are divided into two sections in terms of the key strategies employed to construct the basic skeleton. One is the convergent synthesis route with two synthons coupled by nucleophilic or electrophilic reaction, and the other is the linear synthesis route with polyene series cyclization as a key reaction

Recent Developments in Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, as well as a low number of new drugs that are approved each year. To solve these problems, new and innovate technologies are needed that make the drug discovery process of small-molecules more time and cost-efficient, and which allow to target previously undruggable target classes such as protein-protein interactions. Structure-based virtual screenings have become a leading contender in this context. In this review, we give an introduction to the foundations of structure-based virtual screenings, and survey their progress in the past few years. We outline key principles, recent success stories, new methods, available software, and promising future research directions. Virtual screenings have an enormous potential for the development of new small-molecule drugs, and are already starting to transform early-stage drug discovery.Comment: 22 pages, 2 figure

Ruthenium(II)-bis(4'-(4-ethynylphenyl)-2,2':6', 2''-terpyridine) - A versatile synthon in supramolecular chemistry. Synthesis and characterization

A homoleptic ethynyl-substituted ruthenium(II)-bisterpyridine complex representing a versatile synthon in supramolecular chemistry was synthesized and analyzed by NMR spectroscopy, mass spectrometry and X-ray diffractometry. Furthermore, its photophysical properties were detailed by UV/Vis absorption, emission and resonance Raman spectroscopy. In order to place the results obtained in the context of the vast family of ruthenium coordination compounds, two structurally related complexes were investigated accordingly. These reference compounds bear either no or an increased chromophore in the 4̀-position. The spectroscopic investigations reveal a systematic bathochromic shift of the absorption and emission maximum upon increasing chromophore size. This bathochromic shift of the steady state spectra occurs hand in hand with increasing resonance Raman intensities upon excitation of the metal-to-ligand charge-transfer transition. The latter feature is accompanied by an increased excitation delocalization over the chromophore in the 4̀-position of the terpyridine. Thus, the results presented allow for a detailed investigation of the electronic effects of the ethynyl substituent on the metal-to-ligand charge-transfer states in the synthon for click reactions leading to coordination polymers

Synthesis and applications of theranostic oligonucleotides carrying multiple fluorine atoms

The use of various oligonucleotide (ON) syntheses and post-synthetic strategies for targeted chemical modification enables improving their efficacy as potent modulators of gene expression levels in eukaryotic cells. However, the search still continues for new approaches designed for increasing internalization, lysosomal escape, and tissue specific delivery of ON. In this review we emphasized all aspects related to the synthesis and properties of ON derivatives carrying multifluorinated (MF) groups. These MF groups have unique physico-chemical properties because of their simultaneous hydrophobicity and lipophobicity. Such unusual combination of properties results in the overall modification of ON mode of interaction with the cells and making multi-fluorination highly relevant to the goal of improving potency of ON as components of new therapies. The accumulated evidence so far is pointing to high potential of ON probes, RNAi components and ON imaging beacons carrying single or multiple MF groups for improving the stability, specificity of interaction with biological targets and delivery of ONs in vitro and potentially in vivo

An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries

Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets. However, they are quickly approaching a size beyond that which permits explicit enumeration, presenting new challenges for virtual screening. To overcome these challenges, we propose the Combinatorial Synthesis Library Variational Auto-Encoder (CSLVAE). The proposed generative model represents such libraries as a differentiable, hierarchically-organized database. Given a compound from the library, the molecular encoder constructs a query for retrieval, which is utilized by the molecular decoder to reconstruct the compound by first decoding its chemical reaction and subsequently decoding its reactants. Our design minimizes autoregression in the decoder, facilitating the generation of large, valid molecular graphs. Our method performs fast and parallel batch inference for ultra-large synthesis libraries, enabling a number of important applications in early-stage drug discovery. Compounds proposed by our method are guaranteed to be in the library, and thus synthetically and cost-effectively accessible. Importantly, CSLVAE can encode out-of-library compounds and search for in-library analogues. In experiments, we demonstrate the capabilities of the proposed method in the navigation of massive combinatorial synthesis libraries.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS 2022

RLSynC: Offline-Online Reinforcement Learning for Synthon Completion

Retrosynthesis is the process of determining the set of reactant molecules that can react to form a desired product. Semi-template-based retrosynthesis methods, which imitate the reverse logic of synthesis reactions, first predict the reaction centers in the products, and then complete the resulting synthons back into reactants. These methods enable necessary interpretability and high practical utility to inform synthesis planning. We develop a new offline-online reinforcement learning method RLSynC for synthon completion in semi-template-based methods. RLSynC assigns one agent to each synthon, all of which complete the synthons by conducting actions step by step in a synchronized fashion. RLSynC learns the policy from both offline training episodes and online interactions which allow RLSynC to explore new reaction spaces. RLSynC uses a forward synthesis model to evaluate the likelihood of the predicted reactants in synthesizing a product, and thus guides the action search. We compare RLSynC with the state-of-the-art retrosynthesis methods. Our experimental results demonstrate that RLSynC can outperform these methods with improvement as high as 14.9% on synthon completion, and 14.0% on retrosynthesis, highlighting its potential in synthesis planning.Comment: 11 pages, 8 figures, 6 table