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In this article we report about a successful application of modern machine learning technology, namely Support Vector Machines, to the problem of assessing the 'drug-likeness' of a chemical from a given set of descriptors of the Substance. We were able to drastically improve the recent result by Byvatov et al. (2003) on this task and achieved an error rate of about 7% on unseen compounds using Support Vector Machines. We see a very high potential of such machine learning techniques for a variety of computational chemistry problems that occur in the drug discovery and drug design process
We investigate the cononsolvency effect of poly(N-isopropylacrylamide) (PNIPAM) in mixtures of water and methanol. Two systems are studied: micellar solutions of polystyrene-b-poly(N-isopropylacrylamide) (PS-b-PNIPAM) diblock copolymers and, as a reference, solutions of PNIPAM homopolymers, both at a concentration of 20 mg/mL in DO. Using a stopped-flow instrument, fully deuterated methanol was rapidly added to these solutions at volume fractions between 10 and 20%. Time-resolved turbidimetry revealed aggregate formation within 10-100 s. The structural changes on mesoscopic length scales were followed by time-resolved small-angle neutron scattering (TR-SANS) with a time resolution of 0.1 s. In both systems, the pathway of the aggregation depends on the content of deuterated methanol; however, it is fundamentally different for homopolymer and diblock copolymer solutions: In the former, very large aggregates (>150 nm) are formed within the dead time of the setup, gradient appears at their surface in the late stages. In contrast, the growth of the aggregates in the latter system features different regimes, and the final aggregate size is 50 nm, thus much smaller than for the homopolymer. For the diblock copolymer, the time dependence of the aggregate radius can be described by two models: In the initial stage, the diffusion-limited coalescence model describes the data well; however, the resulting coalescence time is unreasonably high. In the late stage, a logarithmic coalescence model based on an energy barrier which is proportional to the aggregate radius is successfully applied. and a concentration
Fully renewable pyridinium ionic liquids were synthesised via the hydrothermal decarboxylation of pyridinium zwitterions derived from furfural and amino acids in flow. The functionality of the resulting ionic liquid (IL) can be tuned by choice of different amino acids as well as different natural carboxylic acids as the counterions. A representative member of this new class of ionic liquids was successfully used for the synthesis of ionogels and as a solvent for the Heck coupling.
Fully renewable pyridinium ionic liquids were synthesised via the hydrothermal decarboxylation of pyridinium zwitterions derived from furfural and amino acids in flow. The functionality of the resulting ionic liquid (IL) can be tuned by choice of different amino acids as well as different natural carboxylic acids as the counter-ions. A representative member of this new class of ionic liquids was successfully used for the synthesis of ionogels and as a solvent for the Heck coupling.
Fully renewable pyridinium ionic liquids were synthesised via the hydrothermal decarboxylation of pyridinium zwitterions derived from furfural and amino acids in flow. The functionality of the resulting ionic liquid (IL) can be tuned by choice of different amino acids as well as different natural carboxylic acids as the counterions. A representative member of this new class of ionic liquids was successfully used for the synthesis of ionogels and as a solvent for the Heck coupling.