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

• optimization of chemical syntheses with multiple objective functions. For chemical reaction optimization, the Thompson sampling efficient multiobjective optimization (TSEMO) algorithm has been the most widely used due to its capability to model noisy functions, efficient computation, and ability to model

Efficient synthesis of fluorinated triphenylenes with enhanced arene–perfluoroarene interactions in columnar mesophases

• Yang Chen,
• Jiao He,
• Hang Lin,
• Hai-Feng Wang,
• Ping Hu,
• Bi-Qin Wang,
• Ke-Qing Zhao and
• Bertrand Donnio

Beilstein J. Org. Chem. 2024, 20, 3263–3273, doi:10.3762/bjoc.20.270

• intermolecular slipped J-type aggregation, supported by S/WAXS and proposed the model of Colrec mesophase [55][56][57]. DFT computation For a deeper understanding of the electronic properties of these fluorine triphenylenes, we performed some DFT computation of F1 and G11 with the shortest methyl chain, and the

Controlled oligomerization of [1.1.1]propellane through radical polarity matching: selective synthesis of SF₅- and CF₃SF₄-containing [2]staffanes

• Jón Atiba Buldt,
• Wang-Yeuk Kong,
• Yannick Kraemer,
• Masiel M. Belsuzarri,
• Ansh Hiten Patel,
• James C. Fettinger,
• Dean J. Tantillo and
• Cody Ross Pitts

Beilstein J. Org. Chem. 2024, 20, 3134–3143, doi:10.3762/bjoc.20.259

• chloropentafluorosulfanylated [2]staffane (SF5-BCP-BCP-Cl, 2), based on alternating radical polarity matching in the chain-propagation steps (Figure 1, bottom) [45][46][47]. Density functional theory (DFT) calculations provide insight into our observed selectivity, and our hypothesis is bolstered by computation of relative

Computational design for enantioselective CO₂ capture: asymmetric frustrated Lewis pairs in epoxide transformations

• Maxime Ferrer,
• Iñigo Iribarren,
• Tim Renningholtz,
• Ibon Alkorta and
• Cristina Trujillo

Beilstein J. Org. Chem. 2024, 20, 2668–2681, doi:10.3762/bjoc.20.224

• Maxime Ferrer Inigo Iribarren Tim Renningholtz Ibon Alkorta Cristina Trujillo Instituto de Química Médica (CSIC), Juan de la Cierva, 3, 28006 Madrid, Spain Technische Universität München (TUM), School of Computation, Information and Technology, D-85748 Garching, Germany Department of Chemistry

• centres are indicative of the FLP’s reactivity [45][46]. Thus, substituents must be selected to ensure a broad spectrum of acidity and basicity of the LA and LB. Different methods for determining these properties have been described in the literature. Because of their easy computation, the proton affinity

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

• binary fingerprint by comparing the symmetric difference of two sets of circular molecular substructures. The DRFP fingerprint can be seen as the reaction version of the ECFP molecular fingerprint [103]. Due to its fast computation and compatibility with conventional ML models, it has been widely used or

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

Finding the most potent compounds using active learning on molecular pairs

• Zachary Fralish and
• Daniel Reker

Beilstein J. Org. Chem. 2024, 20, 2152–2162, doi:10.3762/bjoc.20.185

• active learning is typically conducted for smaller datasets and in early project stages, we foresee that this combinatorial data expansion will be feasible for most active learning pipelines. Furthermore, as real-world experimentation often provides a larger bottleneck than computation, the use of more

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

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

• quantum chemistry (QM)-based workflow for the automatic computation of C–H pKa values in DMSO. The computed C–H pKa values are then used to generate training data for an ML model using LightGBM [6]. The QM-based workflow and the ML model are freely available under the MIT license. Methods Datasets We

Computation-guided scaffold exploration of 2E,6E-1,10-trans/cis-eunicellanes

• Zining Li,
• Sana Jindani,
• Volga Kojasoy,
• Teresa Ortega,
• Erin M. Marshall,
• Khalil A. Abboud,
• Sandra Loesgen,
• Dean J. Tantillo and
• Jeffrey D. Rudolf

Beilstein J. Org. Chem. 2024, 20, 1320–1326, doi:10.3762/bjoc.20.115

Diameter-selective extraction of single-walled carbon nanotubes by interlocking with Cu-tethered square nanobrackets

• Guoqing Cheng and
• Naoki Komatsu

Beilstein J. Org. Chem. 2024, 20, 1298–1307, doi:10.3762/bjoc.20.113

• , rt, 1.5 h; iv) p-chloranil, dichloromethane, rt, 5 h. Supporting Information Supporting Information File 86: Details of theoretical calculations, experimental procedures, copies of 1H and 13C NMR spectra. Acknowledgements Computation time was provided by supercomputer system, Academic Center for

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

• Akanksha Ashok Sangolkar Rama Krishna Kadiyam Ravinder Pawar Laboratory of Advanced Computation and Theory for Materials and Chemistry, Department of Chemistry, National Institute of Technology Warangal (NITW), Warangal, Telangana-506004, India 10.3762/bjoc.20.93 Abstract Harnessing solar energy

Enhanced host–guest interaction between [10]cycloparaphenylene ([10]CPP) and [5]CPP by cationic charges

• Eiichi Kayahara,
• Yoshiyuki Mizuhata and
• Shigeru Yamago

Beilstein J. Org. Chem. 2024, 20, 436–444, doi:10.3762/bjoc.20.38

• Information File 47: Experimental procedures and computation data. Supporting Information File 48: Crystallographic information file for [10]CPP⊃[5]CPP2+[B(C6F5)4]2. Acknowledgements The calculation was supported by the Super Computer Laboratory, Institute for Chemical Research, Kyoto University. Funding

Thienothiophene-based organic light-emitting diode: synthesis, photophysical properties and application

• Recep Isci and
• Turan Ozturk

Beilstein J. Org. Chem. 2023, 19, 1849–1857, doi:10.3762/bjoc.19.137

• computation data, 1H and 13C NMR spectra. Acknowledgements The authors thank Dilara Gunturkun for DFT calculations, Istanbul Technical University (ITU). Funding The authors thank THD-2023-44904, PTA-2023-44900, TGA-2023-44077, TDA-2022-43696 numbered ITU BAP Projects and 122Z568 numbered TUBITAK 1001 Project

Exploring the role of halogen bonding in iodonium ylides: insights into unexpected reactivity and reaction control

• Carlee A. Montgomery and
• Graham K. Murphy

Beilstein J. Org. Chem. 2023, 19, 1171–1190, doi:10.3762/bjoc.19.86

• electrostatic potentials at specific points on the atom’s surface can be quantitatively determined through either computation or diffraction techniques [44][45]. Computation of the surface’s electrostatic potential extrema (VS,max and VS,min) are based on Equation 1 [31], which considers the contributions of

The effect of dark states on the intersystem crossing and thermally activated delayed fluorescence of naphthalimide-phenothiazine dyads

• Liyuan Cao,
• Xi Liu,
• Xue Zhang,
• Jianzhang Zhao,
• Fabiao Yu and
• Yan Wan

Beilstein J. Org. Chem. 2023, 19, 1028–1046, doi:10.3762/bjoc.19.79

Synthesis, α-mannosidase inhibition studies and molecular modeling of 1,4-imino-ᴅ-lyxitols and their C-5-altered N-arylalkyl derivatives

• Martin Kalník,
• Sergej Šesták,
• Juraj Kóňa,
• Maroš Bella and
• Monika Poláková

Beilstein J. Org. Chem. 2023, 19, 282–293, doi:10.3762/bjoc.19.24

• successful [25][26][27]. The only exception is the latest study by Cheng et al. who reported breakthrough findings in the development of highly potent and selective human GMII inhibitors. A combination of natural product-inspired combinatorial chemistry and computation-guided synthesis provided a nanomolar

Insight into oral amphiphilic cyclodextrin nanoparticles for colorectal cancer: comprehensive mathematical model of drug release kinetic studies and antitumoral efficacy in 3D spheroid colon tumors

• Sedat Ünal,
• Gamze Varan,
• Juan M. Benito,
• Yeşim Aktaş and
• Erem Bilensoy

Beilstein J. Org. Chem. 2023, 19, 139–157, doi:10.3762/bjoc.19.14

• . HPLC was used to quantify the cumulative percentage of CPT released for each time point [9]. Release kinetics study In vitro release profiles of CD nanoparticles were analyzed using DDSolver 1.0 [73], designed to reduce computation time and minimize computational errors, and the data were fitted to

Improving the accuracy of ³¹P NMR chemical shift calculations by use of scaling methods

• William H. Hersh and
• Tsz-Yeung Chan

Beilstein J. Org. Chem. 2023, 19, 36–56, doi:10.3762/bjoc.19.4

• use of the locally dense basis set approach, in which the larger pcS-3 basis set was used on phosphorus and the smaller pcS-2 basis set was then used on all the other atoms, allowed the computation of chemical shifts for a variety of benchmark compounds up to 35-atom triphenylphosphine oxide

Naphthalimide-phenothiazine dyads: effect of conformational flexibility and matching of the energy of the charge-transfer state and the localized triplet excited state on the thermally activated delayed fluorescence

• Kaiyue Ye,
• Liyuan Cao,
• Davita M. E. van Raamsdonk,
• Zhijia Wang,
• Jianzhang Zhao,
• Daniel Escudero and
• Denis Jacquemin

Beilstein J. Org. Chem. 2022, 18, 1435–1453, doi:10.3762/bjoc.18.149

1,4,6,10-Tetraazaadamantanes (TAADs) with N-amino groups: synthesis and formation of boron chelates and host–guest complexes

• Artem N. Semakin,
• Ivan S. Golovanov,
• Yulia V. Nelyubina and
• Alexey Yu. Sukhorukov

Beilstein J. Org. Chem. 2022, 18, 1424–1434, doi:10.3762/bjoc.18.148

• spectra, X-ray data, and computation data. Supporting Information File 278: Crystallographic information files (CIFs) for compounds 19e·3HCl·H2O, Bn-4c(bromide)·3CD3OD, 21·2D2O, 4a·H2O·0.5MeOH, 4a·HCl·H2O·MeOH, 4c·HCl·3.5MeOH, 8a, 21·HCl·2H2O, and hydrazinium dihydrochloride. CCDC deposition numbers

Electrochemical and spectroscopic properties of twisted dibenzo[g,p]chrysene derivatives

• Tomoya Imai,
• Ryuhei Akasaka,
• Naruhiro Yoshida,
• Toru Amaya and
• Tetsuo Iwasawa

Beilstein J. Org. Chem. 2022, 18, 963–971, doi:10.3762/bjoc.18.96

• optimized structures obtained based on the theoretical calculation. Acknowledgements The computation was performed using Research Center for Computational Science, Okazaki, Japan (Project: 21-IMS-C190, 22-IMS-C174). We thank Prof. Shinobu Aoyagi at Graduate School of Science, Nagoya City University for the

Introducing a new 7-ring fused diindenone-dithieno[3,2-b:2',3'-d]thiophene unit as a promising component for organic semiconductor materials

• Valentin H. K. Fell,
• Joseph Cameron,
• Alexander L. Kanibolotsky,
• Eman J. Hussien and
• Peter J. Skabara

Beilstein J. Org. Chem. 2022, 18, 944–955, doi:10.3762/bjoc.18.94

• highly delocalised bipolaron states [69]. Computation of structure Since all attempts to grow crystals of EtH-T-DI-DTT failed, we predicted the structure with a density functional theory (DFT) gas-phase optimisation using the B3LYP[70][71]/6-311g(d,p) [72] level of theory, using both the Gaussian09 [73

First series of N-alkylamino peptoid homooligomers: solution phase synthesis and conformational investigation

• Maxime Pypec,
• Laurent Jouffret,
• Claude Taillefumier and
• Olivier Roy

Beilstein J. Org. Chem. 2022, 18, 845–854, doi:10.3762/bjoc.18.85

• Information Supporting Information File 67: Experimental procedures, HPLC analytical data, NMR spectra and variable concentration study, infrared spectra, full X-ray data for 2, computation data, and additional TEM images for compound 2. Supporting Information File 68: Crystallographic information file (CIF

Syntheses of novel pyridine-based low-molecular-weight luminogens possessing aggregation-induced emission enhancement (AIEE) properties

• Masayori Hagimori,
• Tatsusada Yoshida,
• Yasuhisa Nishimura,
• Yukiko Ogawa and
• Keitaro Tanaka

Beilstein J. Org. Chem. 2022, 18, 580–587, doi:10.3762/bjoc.18.60

• for compounds 3a, 4a, and 4e using the TD-B3LYP/6-311+G(d,p)//B3LYP/6-31G(d,p) level of theory. Supporting Information Supporting Information File 34: General information, synthesis of 3a,b, and 4a–e, experimental procedure of fluorescence, theoretical computation method measurements, NMR, UV–vis