Steric “attraction”: not by dispersion alone

• Ganna Gryn’ova and
• Clémence Corminboeuf

Beilstein J. Org. Chem. 2018, 14, 1482–1490, doi:10.3762/bjoc.14.125

• functional theory [39], post-Hartree–Fock [40][41], symmetry adapted perturbation theory (SAPT) [42][43][44][45][46] data or to a combination of the latter two (e.g., the monomer electron density force field, MEDFF) [47]. The latter approach has been subsequently exploited in the machine learning

Biomimetic molecular design tools that learn, evolve, and adapt

• David A Winkler

Beilstein J. Org. Chem. 2017, 13, 1288–1302, doi:10.3762/bjoc.13.125

• , evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design

• methods and their potential impacts in chemistry, engineering, and medicine. Keywords: automated chemical synthesis; deep learning; evolutionary algorithms; in silico evolution; machine learning; materials design and development; neural networks; Introduction There is still not a clear understanding of

• future impact. It introduces the most common type of algorithm, machine learning. A discussion of a very useful machine-learning algorithm, the neural network follows, and problems that often arise in their use, and solutions to these difficulties described. A new type of deep learning neural network

Automating multistep flow synthesis: approach and challenges in integrating chemistry, machines and logic

• Chinmay A. Shukla and
• Amol A. Kulkarni

Beilstein J. Org. Chem. 2017, 13, 960–987, doi:10.3762/bjoc.13.97

Self-optimisation and model-based design of experiments for developing a C–H activation flow process

• Alexander Echtermeyer,
• Yehia Amar,
• Jacek Zakrzewski and
• Alexei Lapkin

Beilstein J. Org. Chem. 2017, 13, 150–163, doi:10.3762/bjoc.13.18

• model-based design of experiments, based on the first principles model structure, in automated flow experiments, and coupling of the process models with a statistical machine learning based target optimisation. We demonstrate that MBDoE offers a significant potential for efficient and rapid generation

• difficulties regarding multi-objective global optimisation can be overcome. Furthermore, the proposed optimisation procedure can deal with potential uncertainties and restricted validity in the physical model. This is achieved by the machine learning functionalities of the MOAL algorithm, which retrain the

Computational methods in drug discovery

• Sumudu P. Leelananda and
• Steffen Lindert

Beilstein J. Org. Chem. 2016, 12, 2694–2718, doi:10.3762/bjoc.12.267

• ; machine learning; pharmacophore; QSAR; SBDD; scoring; target flexibility; Introduction Bringing a pharmaceutical drug to the market is a long term process that costs billions of dollars. In 2014, the Tufts Center for the Study of Drug Development estimated that the cost associated with developing and

• generates sequence–template alignments for a query sequence and identifies best structure matches from the PDB [53]. In addition to sequence profile alignments, it also uses multiple structure information as well. DescFold is another webserver which employs SVM-based machine learning algorithms in protein

The Beilstein Journal of Organic Chemistry and the changing face of scientific publishing

• Martin G. Hicks and
• Peter H. Seeberger

Beilstein J. Org. Chem. 2015, 11, 2242–2244, doi:10.3762/bjoc.11.242

• , efficiency of peer review and publishing. Text and data mining, big data and machine learning, will also become routinely possible, but will only become really useful if the scientific community starts storing and making all verified results – including the negative – publically available. In organic