In this account, a rapid retrosynthesis-based scoring method for the assessment of synthetic accessibility of drug-like molecules, called RASA (Retrosynthesis-based Assessment of Synthetic Accessibility) is devised. RASA first constructs a synthesis tree for the target molecule based on retrosynthetic analysis; in this process a series of strategies are suggested for limiting combinatorial explosion of the synthesis tree. A scoring function (RASA-score) for the assessment of synthetic accessibility is then proposed based on the optional effective synthetic routes, the complexity of reaction, and the difficulty of separation/purification associated with the most favorable synthetic route. The contributions of individual components are calibrated by linear regression analysis based on the synthetic accessibility estimates of a training set (100 compounds) given by a group of medicinal chemists (G1). Two external test sets (TS1 and TS2), whose synthetic accessibility estimates were given by the group G1 medicinal chemists and another group (G2) of medicinal chemists (from literature), respectively, were adopted for the evaluation of RASA. The correlation coefficient between the calculated RASA-score values and the estimated scores by medicinal chemists for TS1 is 0.807 and that for TS2 is 0.792, which demonstrate the validity and reliability of RASA. The validity and reliability as well as the high speed of RASA and its capability of suggesting synthetic routes enable it a useful tool in drug discovery.

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To better restore human hand function, advanced hand prostheses should be able to deal with a variety of daily living conditions. In this paper, we addressed myoelectric signal variations introduced by different muscle contractions, dynamic arm movements, and outer interfering forces in the practice of pattern recognition-based myoelectric control schemes. We examined four different training paradigms (data-collection protocols) and quantified their effectiveness for obtaining a robust classification. We further depicted the classification accuracy according to different arm/wrist motion primitives. Our results indicate the training paradigm that collects myoelectric signals on dynamic arm postures and varying muscular contractions (DPDE) can largely mitigate the motions' misclassification rate. The misclassification rate of finger motions seems to highly correlate to wrist pronation and supination, rather than different arm positions. Combining proprioceptive information, such as the hand's orientation, with myoelectric signals for classification only slightly alleviates the misclassification rate.

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