BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology.

BindingDB, www.bindingdb.org, is a publicly accessible database of experimental protein-small molecule interaction data. Its collection of over a million data entries derives primarily from scientific articles and, increasingly, US patents. BindingDB provides many ways to browse and search for data of interest, including an advanced search tool, which can cross searches of multiple query types, including text, chemical structure, protein sequence and numerical affinities. The PDB and PubMed provide links to data in BindingDB, and vice versa; and BindingDB provides links to pathway information, the ZINC catalog of available compounds, and other resources. The BindingDB website offers specialized tools that take advantage of its large data collection, including ones to generate hypotheses for the protein targets bound by a bioactive compound, and for the compounds bound by a new protein of known sequence; and virtual compound screening by maximal chemical similarity, binary kernel discrimination, and support vector machine methods. Specialized data sets are also available, such as binding data for hundreds of congeneric series of ligands, drawn from BindingDB and organized for use in validating drug design methods. BindingDB offers several forms of programmatic access, and comes with extensive background material and documentation. Here, we provide the first update of BindingDB since 2007, focusing on new and unique features and highlighting directions of importance to the field as a whole

Statistical mechanics of geomagnetic orientation in sediment bacteria

Also published as: Biological Bulletin 159 (1980): 459-460Last year we reported on time-of-transit experiments in which magnetically orienting bacteria crossed a 1-mm stretch in the direction of a uniform magnetic field. The bacteria were found to behave as tiny self-propelled compass needles subject both to magnetic field alignment and to the randomizing effect of thermal agitation. In strong fields, magnetic bacteria are held in tight aligment; in weaker fields, their swimming paths meander more and transit times are greater. Paul Langevin derived an expression for the distribution of orientation in an ensemble of free-moving dipole particles as a function of ambient field strength. His theory becomes applicable to our experiments when bacterial migration is analyzed as a sequence of short steps during each of which the cell swims in a direction randomly selected from the Langevin distribution . The duration of each step, Δt, is actually a time constant of the cell's loss of directionality due to thermal agitation. By thus treating the migration as a process of random walk with drift, we are able to predict the mean and variance of the time of transit across a 1-mm stretch.Prepared for the Office of Naval Research under Contract N00014-79-C-0071

Creditor Control in Financially Distressed Firms: Empirical Evidence

In this Article we present the results of empirical research that examines how creditor control is manifested in financially troubled firms that have to renegotiate their debt contracts

Rollins Alumni Record, Summer 1977

Dean Charles A. Welch Retire

Target-Free Compound Activity Prediction via Few-Shot Learning

Predicting the activities of compounds against protein-based or phenotypic assays using only a few known compounds and their activities is a common task in target-free drug discovery. Existing few-shot learning approaches are limited to predicting binary labels (active/inactive). However, in real-world drug discovery, degrees of compound activity are highly relevant. We study Few-Shot Compound Activity Prediction (FS-CAP) and design a novel neural architecture to meta-learn continuous compound activities across large bioactivity datasets. Our model aggregates encodings generated from the known compounds and their activities to capture assay information. We also introduce a separate encoder for the unknown compound. We show that FS-CAP surpasses traditional similarity-based techniques as well as other state of the art few-shot learning methods on a variety of target-free drug discovery settings and datasets.Comment: 9 pages, 2 figure