Increasing The Odds Of Hit Iidentification By Screening Against Receptor Homologs

Increasing the odds of hit identification in screening is of significance for drug discovery. The odds for finding a hit are closely related either to the diversity of libraries or to the availability of focused libraries. There are no truly diverse libraries and it is difficult to design focused libraries without sufficient information. Hence it is helpful to consider alternative approaches that can enhance the odds using existing libraries. Multiple members of a protein family have been considered collectively in inhibitor design, on the basis of the correlation between protein families and ligands derived from specific compound classes. Such a correlation has been exploited in various drug discovery studies and a general receptor-homolog-based screening scheme may be devised. The feasibility of such a scheme in enhancing the odds of hit identification is discussed.Singapore-MIT Alliance (SMA

Developing and validating predictive decision tree models from mining chemical structural fingerprints and high–throughput screening data in PubChem

<p>Abstract</p> <p>Background</p> <p>Recent advances in high-throughput screening (HTS) techniques and readily available compound libraries generated using combinatorial chemistry or derived from natural products enable the testing of millions of compounds in a matter of days. Due to the amount of information produced by HTS assays, it is a very challenging task to mine the HTS data for potential interest in drug development research. Computational approaches for the analysis of HTS results face great challenges due to the large quantity of information and significant amounts of erroneous data produced.</p> <p>Results</p> <p>In this study, Decision Trees (DT) based models were developed to discriminate compound bioactivities by using their chemical structure fingerprints provided in the PubChem system <url>http://pubchem.ncbi.nlm.nih.gov</url>. The DT models were examined for filtering biological activity data contained in four assays deposited in the PubChem Bioassay Database including assays tested for 5HT1a agonists, antagonists, and HIV-1 RT-RNase H inhibitors. The 10-fold Cross Validation (CV) sensitivity, specificity and Matthews Correlation Coefficient (MCC) for the models are 57.2~80.5%, 97.3~99.0%, 0.4~0.5 respectively. A further evaluation was also performed for DT models built for two independent bioassays, where inhibitors for the same HIV RNase target were screened using different compound libraries, this experiment yields enrichment factor of 4.4 and 9.7.</p> <p>Conclusion</p> <p>Our results suggest that the designed DT models can be used as a virtual screening technique as well as a complement to traditional approaches for hits selection.</p

The Text-mining based PubChem Bioassay neighboring analysis

<p>Abstract</p> <p>Background</p> <p>In recent years, the number of High Throughput Screening (HTS) assays deposited in PubChem has grown quickly. As a result, the volume of both the structured information (i.e. molecular structure, bioactivities) and the unstructured information (such as descriptions of bioassay experiments), has been increasing exponentially. As a result, it has become even more demanding and challenging to efficiently assemble the bioactivity data by mining the huge amount of information to identify and interpret the relationships among the diversified bioassay experiments. In this work, we propose a text-mining based approach for bioassay neighboring analysis from the unstructured text descriptions contained in the PubChem BioAssay database.</p> <p>Results</p> <p>The neighboring analysis is achieved by evaluating the cosine scores of each bioassay pair and fraction of overlaps among the human-curated neighbors. Our results from the cosine score distribution analysis and assay neighbor clustering analysis on all PubChem bioassays suggest that strong correlations among the bioassays can be identified from their conceptual relevance. A comparison with other existing assay neighboring methods suggests that the text-mining based bioassay neighboring approach provides meaningful linkages among the PubChem bioassays, and complements the existing methods by identifying additional relationships among the bioassay entries.</p> <p>Conclusions</p> <p>The text-mining based bioassay neighboring analysis is efficient for correlating bioassays and studying different aspects of a biological process, which are otherwise difficult to achieve by existing neighboring procedures due to the lack of specific annotations and structured information. It is suggested that the text-mining based bioassay neighboring analysis can be used as a standalone or as a complementary tool for the PubChem bioassay neighboring process to enable efficient integration of assay results and generate hypotheses for the discovery of bioactivities of the tested reagents.</p

Object-Guided Instance Segmentation for Biological Images

Instance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the objects, make instance segmentation a non-trivial task. Current box-free instance segmentation methods typically rely on local pixel-level information. Due to a lack of global object view, these methods are prone to over- or under-segmentation. On the contrary, the box-based instance segmentation methods incorporate object detection into the segmentation, performing better in identifying the individual instances. In this paper, we propose a new box-based instance segmentation method. Mainly, we locate the object bounding boxes from their center points. The object features are subsequently reused in the segmentation branch as a guide to separate the clustered instances within an RoI patch. Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects. Consequently, the proposed model performs excellently in segmenting the clustered objects while retaining the target object details. The proposed method achieves state-of-the-art performances on three biological datasets: cell nuclei, plant phenotyping dataset, and neural cells.Comment: accepted to AAAI202

Dynamic clinical trial success rates for drugs in the 21st century

In clinical drug development, two fundamental questions must be addressed: what is the success rate of drugs in clinical trial; how does such rate change over time. Here, a dynamic strategy for calculating clinical trial success rate (ClinSR) is proposed, which identifies that: the ClinSR has been declining since the early 21st century, yet it hits a plateau and recently starts to increase; the ClinSR for repurposed drugs is unexpectedly lower than that for all drugs in recent years; and an extremely low ClinSR is found for anti-COVID-19 drugs. In-depth analysis reports great variations among the ClinSRs of various diseases, developmental strategies, and drug modalities. A platform ClinSR.org (https://ClinSR.org/), is then developed to show how ClinSRs change over time. All in all, this work enables accurate, timely and continuous assessment of ClinSRs, for now and the future, to aid pharmaceutical and economic decision making

Update of TTD: Therapeutic Target Database

Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets, hundreds of which are targets of approved and clinical trial drugs. Knowledge of these targets and corresponding drugs, particularly those in clinical uses and trials, is highly useful for facilitating drug discovery. Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs. In order to accommodate increasing demand for comprehensive knowledge about the primary targets of the approved, clinical trial and experimental drugs, numerous improvements and updates have been made to TTD. These updates include information about 348 successful, 292 clinical trial and 1254 research targets, 1514 approved, 1212 clinical trial and 2302 experimental drugs linked to their primary targets (3382 small molecule and 649 antisense drugs with available structure and sequence), new ways to access data by drug mode of action, recursive search of related targets or drugs, similarity target and drug searching, customized and whole data download, standardized target ID, and significant increase of data (1894 targets, 560 diseases and 5028 drugs compared with the 433 targets, 125 diseases and 809 drugs in the original release described in previous paper). This database can be accessed at http://bidd.nus.edu.sg/group/cjttd/TTD.asp