Beyond symmetric self-assembly and effective molarity: unlocking functional enzyme mimics with robust organic cages

• Keith G. Andrews

Beilstein J. Org. Chem. 2025, 21, 421–443, doi:10.3762/bjoc.21.30

• -based porous organic cages have been a popular choice for study [377][379][406][407][408], as have MOCs [208][376][386]. Much focus remains on the prediction (and automation) [409] of the formation of cavities by probing combinations of, e.g., amines/aldehydes or metals/ligands to identify structures

• with clear thermodynamic minima [410]. Although this approach might be forward-thinking in terms of materials access, cost, and scale, without precise property prediction it requires serendipity in terms of function-discovery within a “near infinite design space” [376]. Further, by definition

• possibility of larger polarization contributions to catalysis, it also makes property prediction difficult [419] since computational appraisal of nebulous additive effects remains challenging and ungrounded, and difficult to benchmark or validate experimentally. Materials with more precise substrate and

Effect of substitution position of aryl groups on the thermal back reactivity of aza-diarylethene photoswitches and prediction by density functional theory

• Misato Suganuma,
• Daichi Kitagawa,
• Shota Hamatani and
• Seiya Kobatake

Beilstein J. Org. Chem. 2025, 21, 242–252, doi:10.3762/bjoc.21.16

• reactivity, closely matching the experimental data. These findings offer valuable insights for the design of advanced photochromic materials with tailored thermal and photophysical characteristics. Keywords: aza-diarylethene; DFT calculation; photochromism; prediction; thermal back reactivity; Introduction

• theory in combination with the 6-31G(d) basis set well reproduces the experimental value of the activation energy for the thermal back reaction of various diarylbenzenes, resulting in the accurate prediction of the half-lifte time [58][63]. Thus, the combination of experiments and theoretical

• prediction of thermal back reactivity. Moreover, we attempt to find the optimal functional for achieving a high correlation with experimental values by DFT calculation. Results and Discussion Photochromic properties in n-hexane Compounds N1–N3 were synthesized according to the procedures described in the

Quantifying the ability of the CF₂H group as a hydrogen bond donor

• Matthew E. Paolella,
• Daniel S. Honeycutt,
• Bradley M. Lipka,
• Jacob M. Goldberg and
• Fang Wang

Beilstein J. Org. Chem. 2025, 21, 189–199, doi:10.3762/bjoc.21.11

• , the ΔδDMSO–CD3NO2 values of N-methylated CF2H-containing organic salts are generally smaller than those of the corresponding neutral precursors. This observation contradicts our initial prediction that introducing a quaternary nitrogen would enhance the HB donation ability of the CF2H group. It is

Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning

• Pablo Quijano Velasco,
• Kedar Hippalgaonkar and
• Balamurugan Ramalingam

Beilstein J. Org. Chem. 2025, 21, 10–38, doi:10.3762/bjoc.21.3

• -throughput systems or in-house designed reaction modules; (iii) data collection by in-line/offline analytical tools; (iv) mapping the collected data points with the target objectives; (v) prediction of the next set of reaction conditions towards attaining optimal solutions; and (vi) experimental validation

Chemical structure metagenomics of microbial natural products: surveying nonribosomal peptides and beyond

• Thomas Ma and
• John Chu

Beilstein J. Org. Chem. 2024, 20, 3050–3060, doi:10.3762/bjoc.20.253

• silico to avoid rediscovery. They are the cornerstone of chemical structure metagenomics; a few examples in this area of research are described below. NRP biosynthesis and structure prediction NRPs are biosynthesized by either type I or II nonribosomal peptide synthetase (NRPS) [50][51]. Type I NRPS is a

• identity of the amino acid BB it recognizes and activates, can be predicted based on its gene sequence alone [40]. Numerous prediction algorithms of this kind have been reported since [40][41][42][43][44][45][46][47][48][49]. The A domains are highly conserved in terms of both structure and sequence [41

• to the genetic code) [58]. Hundreds of known nonribosomal codes and their corresponding BBs can be extracted from natural products that have been characterized over the past several decades, generating a dataset to train NRP prediction algorithms (Figure 3b) [49][59][60]. A software suite called

Machine learning-guided strategies for reaction conditions design and optimization

• Lung-Yi Chen and
• Yi-Pei Li

Beilstein J. Org. Chem. 2024, 20, 2476–2492, doi:10.3762/bjoc.20.212

• efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry. Keywords: data preprocessing; reaction conditions prediction; reaction data mining; reaction optimization; reaction representation; Introduction Machine learning (ML) techniques have been widely

• models can reproduce patent-derived pathways for known compounds, and even suggest more diverse and efficient alternatives [5][6][7][8]. Building upon the retrosynthesis, the reaction conditions prediction models can help in identifying appropriate conditions for each step, ensuring compatibility with

• the platform and addressing safety concerns. On the other aspect, forward reaction prediction normally plays the role of validating the feasibility of a reaction pathway predicted by retrosynthetic models and to further enhance reaction yields by optimizing reaction parameters such as temperature

Improved deconvolution of natural products’ protein targets using diagnostic ions from chemical proteomics linkers

• Andreas Wiest and
• Pavel Kielkowski

Beilstein J. Org. Chem. 2024, 20, 2323–2341, doi:10.3762/bjoc.20.199

• field have the potential to facilitate this advancement. Overall chemical proteomics strategy to identify protein targets of natural products (NPs) and similar active small compounds. The example protein (blue) is an AlphaFold v2.0-generated prediction of bovine serum albumin (BSA) [23][24]. A) Design

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

• Stefan P. Schmid,
• Leon Schlosser,
• Frank Glorius and
• Kjell Jorner

Beilstein J. Org. Chem. 2024, 20, 2280–2304, doi:10.3762/bjoc.20.196

• . Keywords: catalyst design; machine learning; modelling; organocatalysis; selectivity prediction; Introduction Since the beginning of the 21st century, organocatalysts [1] have established themselves as a third group of homogeneous catalysts, next to biocatalysts [2] (enzymes) and transition metal-based

• , equipping experimentalists with the knowledge necessary to follow the developments in the field. The rest of the review is divided into three parts: (1) ML for reactivity and selectivity prediction, (2) ML for the design of privileged organocatalysts and (3) ML for catalyst and reaction design. Ultimately

• must be considered. Also, for other kind of models, e.g., random forests, it is common practice to consider the importance of individual features for the model’s prediction to gain mechanistic insight. Careful attention must be paid to the collinearity of features [73], such that they are not too

Computational toolbox for the analysis of protein–glycan interactions

• Ferran Nieto-Fabregat,
• Maria Pia Lenza,
• Angela Marseglia,
• Cristina Di Carluccio,
• Antonio Molinaro,
• Alba Silipo and
• Roberta Marchetti

Beilstein J. Org. Chem. 2024, 20, 2084–2107, doi:10.3762/bjoc.20.180

• experimental data. Tools for protein structure prediction Due to the vast conformational space and a complex energy function, protein structure prediction (PSP) is a computationally challenging task. Homology modelling is a template-based PSP that may be used to predict the 3D structure of a protein based on

• modelling, and some of the most popular include: 1. AlphaFold2 [88]: It is an open-access protein structure prediction system based on artificial intelligence and machine learning. It is based on a neural network that can predict the 3D protein structure at a high accuracy level. The AlphaFold solution is

• structure prediction with template-based modelling. It is known for its ability to predict both the structure and function of a protein. It is based on identifying structural templates from the PDB by several threading methodologies with full-length atomic models (https://zhanggroup.org/I-TASSER/). 3

Allostreptopyrroles A–E, β-alkylpyrrole derivatives from an actinomycete Allostreptomyces sp. RD068384

• Marwa Elsbaey,
• Naoya Oku,
• Mohamed S. A. Abdel-Mottaleb and
• Yasuhiro Igarashi

Beilstein J. Org. Chem. 2024, 20, 1981–1987, doi:10.3762/bjoc.20.174

• -configurations were proposed for compounds 2 and 3. However, this prediction was not confirmed by chemical derivatization due to their limited availability. 1H and 13C NMR spectra of compounds 4 and 5 were superimposable to those of 1 except for methylene resonances, supporting that both 4 and 5 possess the same

Novel oxidative routes to N-arylpyridoindazolium salts

• Oleg A. Levitskiy,
• Yuri K. Grishin and
• Tatiana V. Magdesieva

Beilstein J. Org. Chem. 2024, 20, 1906–1913, doi:10.3762/bjoc.20.166

• [13][14][15][16][17][18][19], N,N-diarylbenzidines [20][21], N,N-diaryldihydrophenazines [20][21] and some others. Therefore, the selectivity issue is of primary importance. The guidelines for prediction of the dominant reaction path in the competing oxidative transformations of variously substituted

The Groebke–Blackburn–Bienaymé reaction in its maturity: innovation and improvements since its 21st birthday (2019–2023)

• Cristina Martini,
• Muhammad Idham Darussalam Mardjan and
• Andrea Basso

Beilstein J. Org. Chem. 2024, 20, 1839–1879, doi:10.3762/bjoc.20.162

pKalculator: A pK_a predictor for C–H bonds

• Rasmus M. Borup,
• Nicolai Ree and
• Jan H. Jensen

Beilstein J. Org. Chem. 2024, 20, 1614–1622, doi:10.3762/bjoc.20.144

• classifier as Ree et al. [14] have shown the opposite to be true for electrophilic aromatic substitutions. However, our regression model serves a dual function, that is, it accurately predicts pKa values and identifies the reaction site. Prediction of aryl C–H borylation sites In the previous section, we

• reported borylation reactions [45]. Arrow: major experimental site/prediction by SoBo; black ring: QM-computed lowest pKa + 1.5; teal filled circle: ML-predicted lowest pKa + 1.5. Predicting the reaction site for three different reactions from the out-of-sample dataset from Reaxys. (a) Aldol reaction

Bioinformatic prediction of the stereoselectivity of modular polyketide synthase: an update of the sequence motifs in ketoreductase domain

• Changjun Xiang,
• Shunyu Yao,
• Ruoyu Wang and
• Lihan Zhang

Beilstein J. Org. Chem. 2024, 20, 1476–1485, doi:10.3762/bjoc.20.131

• medicines such as erythromycin and rapamycin. They are often rich in stereocenters biosynthesized by the ketoreductase (KR) domain within the polyketide synthase (PKS) assembly line. Previous studies have identified conserved motifs in KR sequences that enable the bioinformatic prediction of product

• stereochemistry. However, the reliability and applicability of these prediction methods have not been thoroughly assessed. In this study, we conducted a comprehensive bioinformatic analysis of 1,762 KR sequences from cis-AT PKSs to reevaluate the residues involved in conferring stereoselectivity. Our findings

• stereochemical outcomes of KRs. These updated fingerprint motifs for stereochemical prediction not only enhance our understanding of the enzymatic mechanisms governing stereocontrol but also facilitate accurate stereochemical prediction and genome mining of polyketides derived from modular cis-AT PKSs. Keywords

Predicting bond dissociation energies of cyclic hypervalent halogen reagents using DFT calculations and graph attention network model

• Yingbo Shao,
• Zhiyuan Ren,
• Zhihui Han,
• Li Chen,
• Yao Li and
• Xiao-Song Xue

Beilstein J. Org. Chem. 2024, 20, 1444–1452, doi:10.3762/bjoc.20.127

• (ANN) models [57] with the Mordred cheminformatics package have demonstrated considerable success in predicting the BDEs of hypervalent iodine(III) reagents. However, previous studies have been limited to the prediction of hypervalent iodine(III) reagents. Driven by their proven effectiveness and our

• ) model architecture and workflow; c) comparison of prediction performance on the training set using different descriptors (see more details in Supporting Information File 1); d) prediction performance for homolytic BDEs on the test set; e) prediction performance for heterolytic BDEs on the test set

A comparison of structure, bonding and non-covalent interactions of aryl halide and diarylhalonium halogen-bond donors

• Nicole Javaly,
• Theresa M. McCormick and
• David R. Stuart

Beilstein J. Org. Chem. 2024, 20, 1428–1435, doi:10.3762/bjoc.20.125

• parameter coefficients reveals that Vs,max is a more dominant term than % p-orbital character in predicting ΔG (Equation 1 and Scheme 7). However, the % p-character term is non-negligible and demonstrates that this highly intuitive parameter contributes to the prediction of ΔG for halogen bonding. It is

• important to point out that this model is limited to the halogen-bond donors studied here and their interaction with chloride anion, although it is likely that prediction of ΔG with Equation 1 for structurally similar halogen-bond donors would be successful provided the parameters (% p-character and Vs,max

Sustainable tandem acylation/Diels–Alder reaction toward versatile tricyclic epoxyisoindole-7-carboxylic acids in renewable green solvents

• Ayhan Yıldırım

Beilstein J. Org. Chem. 2024, 20, 1308–1319, doi:10.3762/bjoc.20.114

• prediction that their resources will run out in the near future has led 'green chemists' to explore solvents that can be derived from renewable resources and used effectively in various organic transformations. In this context, we have shown for the first time that the 100% atom-economical tandem Diels–Alder

Novel route to enhance the thermo-optical performance of bicyclic diene photoswitches for solar thermal batteries

• Akanksha Ashok Sangolkar,
• Rama Krishna Kadiyam and
• Ravinder Pawar

Beilstein J. Org. Chem. 2024, 20, 1053–1068, doi:10.3762/bjoc.20.93

• using the M062X functional in combination with the 6-311++G** Pople-type basis set. This method was employed since the previous benchmarking studies have reported that the M062X/6-311++G** level performs well for prediction of the geometry and photoswitching properties of the BBD based systems. The

Substrate specificity of a ketosynthase domain involved in bacillaene biosynthesis

• Zhiyong Yin and
• Jeroen S. Dickschat

Beilstein J. Org. Chem. 2024, 20, 734–740, doi:10.3762/bjoc.20.67

• introduced during the elongation steps of modules 13–15, and not after complete assembly of the PKS backbone [19]. A phylogenetic analysis of KS domains from trans-AT PKSs revealed that these domains group together, if the structures of their processed substrates are similar, allowing for a prediction of the

New variochelins from soil-isolated Variovorax sp. H002

• Jabal Rahmat Haedar,
• Aya Yoshimura and
• Toshiyuki Wakimoto

Beilstein J. Org. Chem. 2024, 20, 692–700, doi:10.3762/bjoc.20.63

• a result, a total of 0.4 Gbp reads were obtained. The reads were subjected to hybrid de novo assembly using SPAdes (v0.4.8) [23]. The assemblies were subjected to BGC prediction using antiSMASH (6.0.1) [24]. Generation of ΔvarG mutant The 1 kb upstream and downstream regions flanking varG were

Recent developments in the engineered biosynthesis of fungal meroterpenoids

• Zhiyang Quan and
• Takayoshi Awakawa

Beilstein J. Org. Chem. 2024, 20, 578–588, doi:10.3762/bjoc.20.50

• meroterpenoid biosynthetic pathways, enzymatic engineering, and feeding with unnatural substrates. Enzyme crystal structure analysis and AI structure prediction also facilitated these investigations. Besides terpene cyclases and αKG-dependent dioxygenases, P450 monooxygenases and UbiA-type prenyltransferases

Elucidating the glycan-binding specificity and structure of Cucumis melo agglutinin, a new R-type lectin

• Jon Lundstrøm,
• Emilie Gillon,
• Valérie Chazalet,
• Nicole Kerekes,
• Antonio Di Maio,
• Ten Feizi,
• Yan Liu,
• Annabelle Varrot and
• Daniel Bojar

Beilstein J. Org. Chem. 2024, 20, 306–320, doi:10.3762/bjoc.20.31

• melo (variant makuwa, taxon ID: 1194695) [19] and has, to our knowledge, never been studied before. With prediction scores of 0.453 on LectomeXplore [12] and 0.251 on TrefLec [20] (from 0, lowest, to 1, highest), CMA1 is moderately certain in its prior classification as a lectin. CMA1 comprises 291

Comparison of glycosyl donors: a supramer approach

• Anna V. Orlova,
• Nelly N. Malysheva,
• Maria V. Panova,
• Nikita M. Podvalnyy,
• Michael G. Medvedev and
• Leonid O. Kononov

Beilstein J. Org. Chem. 2024, 20, 181–192, doi:10.3762/bjoc.20.18

• donors 1 and 2 performed at the selected concentrations (0.05 and 0.15 mol·L−1). Note that limitations of supramer analysis mentioned above apply here, too. Glycosylation experiments In order to verify this prediction, we then performed two sets of comparative glycosylation experiments with equimolar

Optimizing reaction conditions for the light-driven hydrogen evolution in a loop photoreactor

• Pengcheng Li,
• Daniel Kowalczyk,
• Johannes Liessem,
• Mohamed M. Elnagar,
• Dariusz Mitoraj,
• Radim Beranek and
• Dirk Ziegenbalg

Beilstein J. Org. Chem. 2024, 20, 74–91, doi:10.3762/bjoc.20.9

• actual experimental hydrogen generation rate could be achieved. No significant difference between the prediction accuracy of the two models is observable. The only difference between the two models is that the modified model considers the interaction of parameters. Therefore, to validate the model from

• DOE analysis, experiments that vary only the individual parameters were performed to see which model is more appropriate. Analysis of individual reaction parameters General considerations: To test the validity of the model derived with the DOE approach, the capabilities of the model prediction were

• relationship is also supported by the DOE analysis. Both DOE models predict the experiment results quite well. The linear model yields a slightly better prediction. A linear regression of only the data points shown here shows an expected good agreement with the experimental data. Photocatalyst loading: The

NMRium: Teaching nuclear magnetic resonance spectra interpretation in an online platform

• Luc Patiny,
• Hamed Musallam,
• Alejandro Bolaños,
• Michaël Zasso,
• Julien Wist,
• Metin Karayilan,
• Eva Ziegler,
• Johannes C. Liermann and
• Nils E. Schlörer

Beilstein J. Org. Chem. 2024, 20, 25–31, doi:10.3762/bjoc.20.4

• grasp functionality would make it a preselected tool for the review process of chemistry publications. Future developments of NMRium include the processing of 2D NMR experiments, as well as a CASE- and prediction-supported assignment tool with an integrated validation function for assigned data [44