MAIP: a web service for predicting blood-stage malaria inhibitors.

Malaria is a disease affecting hundreds of millions of people across the world, mainly in developing countries and especially in sub-Saharan Africa. It is the cause of hundreds of thousands of deaths each year and there is an ever-present need to identify and develop effective new therapies to tackle the disease and overcome increasing drug resistance. Here, we extend a previous study in which a number of partners collaborated to develop a consensus in silico model that can be used to identify novel molecules that may have antimalarial properties. The performance of machine learning methods generally improves with the number of data points available for training. One practical challenge in building large training sets is that the data are often proprietary and cannot be straightforwardly integrated. Here, this was addressed by sharing QSAR models, each built on a private data set. We describe the development of an open-source software platform for creating such models, a comprehensive evaluation of methods to create a single consensus model and a web platform called MAIP available at https://www.ebi.ac.uk/chembl/maip/ . MAIP is freely available for the wider community to make large-scale predictions of potential malaria inhibiting compounds. This project also highlights some of the practical challenges in reproducing published computational methods and the opportunities that open-source software can offer to the community.

Evaluating and evolving a screening library in academia: the St Jude approach.

The quality of lead compounds is a key factor for determining the success of chemical probe and drug discovery programs. Given that high-throughput screening (HTS) continues to be a dominant lead generation paradigm, access to high-quality screening libraries is crucial for such efforts in both industry and academia. Here, we discuss the strategy implemented a decade ago to build from scratch one of the largest compound collections in academia, containing ∼575 000 carefully annotated small molecules, and a recent multidisciplinary effort designed to further enhance the collection to meet our research demands for the next decade.

Identification of Compounds with pH-Dependent Bactericidal Activity against Mycobacterium tuberculosis.

To find new inhibitors of Mycobacterium tuberculosis that have novel mechanisms of action, we miniaturized a high throughput screen to identify compounds that disrupt pH homeostasis. We adapted and validated a 384-well format assay to determine intrabacterial pH using a ratiometric green fluorescent protein. We screened 89000 small molecules under nonreplicating conditions and confirmed 556 hits that reduced intrabacterial pH (below pH 6.5). We selected five compounds that disrupt intrabacterial pH homeostasis and also showed some activity against nonreplicating bacteria in a 4-stress model, but with no (or greatly reduced) activity against replicating bacteria. The compounds selected were two benzamide sulfonamides, a benzothiadiazole, a bissulfone, and a thiadiazole, none of which are known antibacterial agents. All of these five compounds demonstrated bactericidal activity against nonreplicating bacteria in buffer. Four of the five compounds demonstrated increased activity under low pH conditions. None of the five compounds acted as ionophores or as general disrupters of membrane potential. These compounds are useful starting points for work to elucidate their mechanism of action and their utility for drug discovery.

The synthesis and antibacterial activity of 1,3,4-thiadiazole phenyl oxazolidinone analogues.

Replacement of the morpholine C-ring of linezolid 1 with a 1,3,4-thiadiazolyl ring leads to oxazolidinone analogues 5 having potent antibacterial activity against both gram-positive and gram-negative organisms. Conversion of the C5 acetamide group to a thioacetamide further increases the potency of these compounds.