Bigger data,collaborative tools and the future of predictive drug discovery

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.     

STUDIES OF ADHESION OF METAL PARTICLES TO SILICA PARTICLES BASED ON ZETA POTENTIAL MEASUREMENTS

The zeta-potentials of silica, copper, platinum and gold particles have been measured as a function of pH. The isoelectric points were found to be at pH 3.0, 5.8, 3.0 and 3.5, respectively. In the pH range 3.0 to 5.8 copper and silica particles are oppositely charged and accordingly the coating of silica with copper particles could be demonstrated. In the case of gold and platinum the sign of the charge is such that direct adhesion to silica particles cannot be expected and this was also demonstrated in the case of platinum.     

<Emphasis Type="Italic">N</Emphasis>-arachidonoyl glycine,an abundant endogenous lipid,potently drives directed cellular migration through GPR18, the putative abnormal cannabidiol receptor

Background  

Microglia provide continuous immune surveillance of the CNS and upon activation rapidly change phenotype to express receptors that respond to chemoattractants during CNS damage or infection. These activated microglia undergo directed migration towards affected tissue. Importantly, the molecular species of chemoattractant encountered determines if microglia respond with pro- or anti-inflammatory behaviour, yet the signaling molecules that trigger migration remain poorly understood. The endogenous cannabinoid system regulates microglial migration via CB₂ receptors and an as yet unidentified GPCR termed the 'abnormal cannabidiol' (Abn-CBD) receptor. Abn-CBD is a synthetic isomer of the phytocannabinoid cannabidiol (CBD) and is inactive at CB₁ or CB₂ receptors, but functions as a selective agonist at this G_i/o-coupled GPCR. N-arachidonoyl glycine (NAGly) is an endogenous metabolite of the endocannabinoid anandamide and acts as an efficacious agonist at GPR18. Here, we investigate the relationship between NAGly, Abn-CBD, the unidentified 'Abn-CBD' receptor, GPR18, and BV-2 microglial migration.     

Lossless compression of chemical fingerprints using integer entropy codes improves storage and retrieval

Many modern chemoinformatics systems for small molecules rely on large fingerprint vector representations, where the components of the vector record the presence or number of occurrences in the molecular graphs of particular combinatorial features, such as labeled paths or labeled trees. These large fingerprint vectors are often compressed to much shorter fingerprint vectors using a lossy compression scheme based on a simple modulo procedure. Here, we combine statistical models of fingerprints with integer entropy codes, such as Golomb and Elias codes, to encode the indices or the run lengths of the fingerprints. After reordering the fingerprint components by decreasing frequency order, the indices are monotone-increasing and the run lengths are quasi-monotone-increasing, and both exhibit power-law distribution trends. We take advantage of these statistical properties to derive new efficient, lossless, compression algorithms for monotone integer sequences: monotone value (MOV) coding and monotone length (MOL) coding. In contrast to lossy systems that use 1024 or more bits of storage per molecule, we can achieve lossless compression of long chemical fingerprints based on circular substructures in slightly over 300 bits per molecule, close to the Shannon entropy limit, using a MOL Elias Gamma code for run lengths. The improvement in storage comes at a modest computational cost. Furthermore, because the compression is lossless, uncompressed similarity (e.g., Tanimoto) between molecules can be computed exactly from their compressed representations, leading to significant improvements in retrival performance, as shown on six benchmark data sets of druglike molecules.     

Utility-aware screening with clique-oriented prioritization

Most methods of deciding which hits from a screen to send for confirmatory testing assume that all confirmed actives are equally valuable and aim only to maximize the number of confirmed hits. In contrast, "utility-aware" methods are informed by models of screeners' preferences and can increase the rate at which the useful information is discovered. Clique-oriented prioritization (COP) extends a recently proposed economic framework and aims--by changing which hits are sent for confirmatory testing--to maximize the number of scaffolds with at least two confirmed active examples. In both retrospective and prospective experiments, COP enables accurate predictions of the number of clique discoveries in a batch of confirmatory experiments and improves the rate of clique discovery by more than 3-fold. In contrast, other similarity-based methods like ontology-based pattern identification (OPI) and local hit-rate analysis (LHR) reduce the rate of scaffold discovery by about half. The utility-aware algorithm used to implement COP is general enough to implement several other important models of screener preferences.