Positioning, Navigation, and Book Accessing/Returning in an Autonomous Library Robot using Integrated Binocular Vision and QR Code Identification Systems.

With rapid advancements in artificial intelligence and mobile robots, some of the tedious yet simple jobs in modern libraries, like book accessing and returning (BAR) operations that had been fulfilled manually before, could be undertaken by robots. Due to the limited accuracies of the existing positioning and navigation (P&N) technologies and the operational errors accumulated within the robot P&N process, however, most of the current robots are not able to fulfill such high-precision operations. To address these practical issues, we propose, for the first time (to the best of our knowledge), to combine the binocular vision and Quick Response (QR) code identification techniques together to improve the robot P&N accuracies, and then construct an autonomous library robot for high-precision BAR operations. Specifically, the binocular vision system is used for dynamic digital map construction and autonomous P&N, as well as obstacle identification and avoiding functions, while the QR code identification technique is responsible for both robot operational error elimination and robotic arm BAR operation determination. Both simulations and experiments are conducted to verify the effectiveness of the proposed technique combination, as well as the constructed robot. Results show that such a technique combination is effective and robust, and could help to significantly improve the P&N and BAR operation accuracies, while reducing the BAR operation time. The implemented autonomous robot is fully-autonomous and cost-effective, and may find applications far beyond libraries with only sophisticated technologies employed.

PaDEL-DDPredictor: open-source software for PD-PK-T prediction.

ADMET (absorption, distribution, metabolism, excretion, and toxicity)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of PD-PK-T properties using in silico tools has become very important in pharmaceutical research to reduce cost and enhance efficiency. PaDEL-DDPredictor is an in silico tool for rapid prediction of PD-PK-T properties of compounds from their chemical structures. It is free and open-source software that, has both graphical user interface and command line interface, can work on all major platforms (Windows, Linux, and MacOS) and supports more than 90 different molecular file formats. The software can be downloaded from http://padel.nus.edu.sg/software/padelddpredictor.

Determination of the Potential of Drug Candidates to Cause Severe Skin Disorders Using Computational Modeling.

Efficient and accurate prediction for drugs' potential to cause rare and severe adverse drug reactions (ADRs) is needed to facilitate the evaluation of risk-benefit ratio of drug candidates during drug development. Severe skin disorders like the Stevens Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), which are life-threatening dermatological conditions, are such ADRs that have not received sufficient attention so far. In this study, a total of 1127 marketed drugs were screened for their potential to cause SJS/TEN, of which 255 were found to cause SJS/TEN and 239 were unlikely to cause SJS/TEN. One-class classification method was used to develop multiple prediction models. An applicability domain was determined to define the applicability of the model. Ensemble method was used to develop ensemble models to improve prediction ability. The final ensemble model achieved a sensitivity and specificity of 81 % and 67.4 %, respectively, when estimated using the external 5-fold cross validation method, and a sensitivity of 66.7 % when assessed using an external positive set. The results suggest the methods used in this study are potentially useful for facilitating the prediction of rare and severe ADRs.

Determination of torsade-causing potential of drug candidates using one-class classification and ensemble modelling approaches.

Adverse drug reactions (ADRs) are a main problem faced by drug companies and regulatory authorities. Not only do they contribute heavily to late-phase failure of drug development and withdrawal of drugs from the market, they also pose significant health risks to patients. Rare and severe ADRs are even harder to detect, and sufficient attention has not been paid to them. Torsade de pointes (TdP), an atypical ventricular tachycardia which is potentially life-threatening, is one of them. The objective of this project is to develop a computational model to predict TdP-causing potential of drug candidates. A total of 260 marketed drugs were collected and screened for their potential to cause TdP. 103 drugs were classified as TdP+ and 157 were likely to be TdP-. One-class classification methods were used to construct multiple base models. A model dependent applicability domain estimation method was used to determine the applicability of the base models for future dataset. A final ensemble model was constructed based on selected base models and it had sensitivity and specificity value of 78.4% and 90% respectively when estimated using external cross validation method. The result suggests that the ensemble model developed in this study is potentially useful for facilitating the prediction of TdP in drug candidates. The ensemble model is made available via the free software, PaDEL-DDPredictor.