MEMES: Machine learning framework for Enhanced MolEcular Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as “hits”. In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.

A novel machine learning framework based on Bayesian optimization for efficient sampling of chemical space. The framework is able to identify 90% of top-1000 hits by only sampling 6% of the complete dataset containing ∼100 million compounds.     

pH-Based Detection of Target Analytes in Diluted Serum Samples Using Surface Plasmon Resonance Immunosensor

Applied Biochemistry and Biotechnology - Detection of minute quantities of target antigens in serum samples (consisting of a mixture of proteins/biomolecules) can be achieved by enhancement of the...     

ɛ‐caprolactone and lactide polymerization promoted by an ionic titanium(IV)/iron(III) polynuclear halo‐alkoxide

The ionic [Ti₃(µ₃‐OPrⁱ)₂(µ‐OPrⁱ)₃(OPrⁱ)₆][FeCl₄] halo‐alkoxide ( A ) was investigated for its activity towards the bulk polymerization of rac‐lactide (rac‐LA) and ?‐caprolactone (?‐CL) in various temperatures, monomer/ A molar proportions, and reaction times. The reactivity of A in the ring‐opening polymerization (ROP) of both monomers is mainly due to the cationic [Ti₃(OPrⁱ)₁₁]⁺ unity and proceeds through the coordination–insertion mechanism. Molecular weights ranging from 6,379 to 13,950 g mol^?1 and PDI values varying from 1.22 to 1.52 were obtained. Results of ROP kinetic studies for both ?‐CL and rac‐LA confirm that the reaction rates are first‐order with respect to monomers. The production of poly(?‐caprolactone) shows a higher sensitivity of the reaction rate to temperature, while the polymerization of rac‐LA is slower and more dependent on the thermal stability of the active species during the propagation step. © 2014 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2014 , 52, 2509–2517     

Constraints on two-dimensional conformal field theories from knot invariants

The connection between Witten's topological three-dimensional gauge theory and RCFTs provides a natural setting to study the interplay between surface diffeomorphisms and intertwining of Wilson loops. These considerations lead directly to constraints on RFCTs including those previously derived by Vafa.