TY - JOUR A1 - Anker, Andy S. A1 - Aspuru-Guzik, Alán A1 - Bechtel, Tim A1 - Bigi, Filippo A1 - Briling, Ksenia R. A1 - Das, Basita A1 - David, Nicholas A1 - Day, Graeme M. A1 - Deringer, Volker L. A1 - Dyer, Matthew A1 - Eardley-Brunt, Annabel A1 - Evans, Matthew L. A1 - Evans, Rob A1 - Franklin, Barnabas A. A1 - Ganose, Alex M. A1 - George, Janine A1 - Goulding, Mark A1 - Hickey, Niamh A1 - James, Gillian A1 - Kalikadien, Adarsh V. A1 - Kapil, Venkat A1 - Kulik, Heather J. A1 - Kumar, Vishank A1 - Kuttner, Christian A1 - Lam, Erwin A1 - Lederbauer, Magdalena A1 - Lou, Yuchen A1 - Martin, Jennie A1 - Marulanda Bran, Andres A1 - Mathea, Miriam A1 - Pickard, Chris J. A1 - Ruscic, Branko A1 - Ryder, Matthew R. A1 - Sabanza Gil, Victor A1 - Schwaller, Philippe A1 - Segler, Marwin H. S. A1 - Sun, Wenhao A1 - Tanovic, Sara A1 - Treyde, Wojtek A1 - Walsh, Aron A1 - Wu, Ruiqi T1 - Discovering synthesis targets: General discussion N2 - This article is a discussion of the paper "Analysis of uncertainty of neural fingerprint-based models" by Christian W. Feldmann, Jochen Sieg and Miriam Mathea (Faraday discussions, 2025, DOI: 10.1039/D4FD00095A). KW - Automation KW - Materials acceleration platforms KW - Machine learning KW - Materials design KW - Materials discovery KW - Density functional theory KW - Ab initio PY - 2025 DO - https://doi.org/10.1039/D4FD90064B SN - 1359-6640 SN - 1364-5498 VL - 256 IS - Themed collection: Data-driven discovery in the chemical sciences SP - 639 EP - 663 PB - Royal Society of Chemistry (RSC) CY - Cambridge AN - OPUS4-62317 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Anker, Andy S. A1 - Aspuru-Guzik, Alán A1 - Ben Mahmoud, Chiheb A1 - Bennett, Sophie A1 - Briling, Ksenia R. A1 - Changiarath, Arya A1 - Chong, Sanggyu A1 - Collins, Christopher M. A1 - Cooper, Andrew I. A1 - Crusius, Daniel A1 - Darmawan, Kevion K. A1 - Das, Basita A1 - David, Nicholas A1 - Day, Graeme M. A1 - Deringer, Volker L. A1 - Duarte, Fernanda A1 - Eardley-Brunt, Annabel A1 - Evans, Matthew L. A1 - Evans, Rob A1 - Fairlamb, Ian A1 - Franklin, Barnabas A. A1 - Frey, Jeremy A1 - Ganose, Alex M. A1 - Goulding, Mark A1 - Hafizi, Roohollah A1 - Hakkennes, Matthijs A1 - Hickey, Niamh A1 - James, Gillian A1 - Jelfs, Kim E. A1 - Kalikadien, Adarsh V. A1 - Kapil, Venkat A1 - Koczor-Benda, Zsuzsanna A1 - Krammer, Ferdinand A1 - Kulik, Heather J. A1 - Kumar, Vishank A1 - Kuttner, Christian A1 - Lam, Erwin A1 - Lou, Yuchen A1 - Mante, Eltjo A1 - Martin, Jennie A1 - Mroz, Austin M. A1 - Nematiaram, Tahereh A1 - Pare, Charles W. P. A1 - Patra, Sarbani A1 - Proudfoot, James A1 - Ruscic, Branko A1 - Ryder, Matthew R. A1 - Sakaushi, Ken A1 - Saßmannshausen, Jörg A1 - Savoie, Brett M. A1 - Schneider, Nadine A1 - Schwaller, Philippe A1 - Skjelstad, Bastian Bjerkem A1 - Sun, Wenhao A1 - Szczypiński, Filip T. A1 - Torrisi, Steven A1 - Ueltzen, Katharina A1 - Vishnoi, Shubham A1 - Walsh, Aron A1 - Wang, Xinwei A1 - Wilson, Chloe A1 - Wu, Ruiqi A1 - Zeitler, Jakob T1 - Discovering structure–property correlations: General discussion N2 - This article is a discussion of the paper "Web-BO: Towards increased accessibility of Bayesian optimisation (BO) for chemistry" by Austin M. Mroz, Piotr N. Toka, Ehecatl Antonio del Río Chanona and Kim E. Jelfs (Faraday discussions, 2025, 256, 221-234). KW - Materials design KW - Machine learning KW - Automation KW - Materials discovery PY - 2025 DO - https://doi.org/10.1039/d4fd90062f SN - 1359-6640 SN - 1364-5498 VL - 256 IS - Themed collection: Data-driven discovery in the chemical sciences SP - 373 EP - 412 PB - Royal Society of Chemistry (RSC) CY - Cambridge AN - OPUS4-62208 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bhattacharya, Biswajit A1 - Das, S. A1 - Lal, G. A1 - Soni, S. R. A1 - Ghosh, A. A1 - Reddy, C. M. A1 - Ghosh, S. T1 - Screening, crystal structures and solubility studies of a series of multidrug salt hydrates and cocrystals of fenamic acids with trimethoprim and sulfamethazine N2 - Multidrug solids have a potential use to efficiently treat and control a superfluity of medical conditions. To address the current drawbacks of drug development in R&D, it was targeted to achieve new pharmaceutical solid forms of fenamic acids having improved solubility and thermal stability. Subsequently, five new multicomponent solids consisting of three salt hydrates of trimethoprim (TMP) with mefenamic acid (TMP-MFA-H2O), tolfenamic acid (TMP-TFA-H2O) and flufenamic acid (TMP-FFA-H2O), and two cocrystals of sulfamethazine (SFZ) with flufenamic acid (SFZ-FFA) and niflumic acid (SFZ-NFA) were prepared by liquid assisted grinding. Looking at the structures of active pharmaceutical ingredient (API) molecules, it was quite expected that a wide range of supramolecular synthons would lead to cocrystallization. New forms were characterized thoroughly by various solid-state techniques, including single crystal X-ray diffraction (SCXRD), which provided details of hydrogen bonding, molecular packing and interactions between drug and coformer. Kinetic solubility at pH 7.4 buffer study has been carried out and a comparison is made with respect to the parent drugs. A significant enhancement of NSAIDs solubility was observed in all salt hydrate systems of TMP. Thus with increasing physicochemical properties such as improved solubility further leads to the enhancement of bioavailability, which has implications to overcoming the formulation related problems of active pharmaceutical ingredients (APIs). KW - Cocrystals KW - Crystal engineering KW - Solubility PY - 2020 DO - https://doi.org/10.1016/j.molstruc.2019.127028 SN - 0022-2860 VL - 1199 SP - 127028 PB - Elsevier B.V. AN - OPUS4-49873 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -