Supervised learning models, also known as quantitative structure-activity regression (QSAR) models, are increasingly used in assisting the process of preclinical, small molecule drug discovery. The models are trained on data consisting of a finite dimensional representation of molecular structures and their corresponding target specific activities. These models can then be used to predict the activity of previously unmeasured novel compounds. In this work we address two problems related to this approach. The first is to estimate the extent to which the quality of the model predictions degrades for compounds very different from the compounds in the training data. The second is to adjust for the screening dependent selection bias inherent in many training data sets. In the most extreme cases, only compounds which pass an activity-dependent screening are reported. By using a semi-supervised learning framework, we show that it is possible to make predictions which take into account the similarity of the testing compounds to those in the training data and adjust for the reporting selection bias. We illustrate this approach using publicly available structure-activity data on a large set of compounds reported by GlaxoSmithKline (the Tres Cantos AntiMalarial Set) to inhibit in vitro P. falciparum growth.

This is a non-commercial article aiming to explain and clarify my findings of the leverage of AI-based trading robots. Experiment fees were paid by myself. Currently, I really need financial support for further executions. Many photos in this article are gathered through Google, please mail to Longfei.Lu@live.com L3 Level Robot Demo can be found - https://twitter.com/aGiant_U2

One of the world's most disruptive industries is voice services. Once dominated by a few large and established players, new innovations in Internet Protocol (IP) communication technologies are making it easier for non-telecom businesses--including software and hardware companies--to compete effectively. To be successful, however, it's not enough for these challengers to have a great idea. They must also be able to focus their efforts on what they do best and get their solutions to market quickly. Level 7 Systems is one such startup.

Flow is a psychological state that is reported to improve people’s performance. Flow can emerge when the person’s skills and the challenges of their activity match. This paper applies this concept to artificial intelligence agents. We equip a decision-making agent with a metacontrol policy that guides the agent to activities where the agent’s skills match the activity difficulty. Consequently, we expect the agent’s performance to improve. We implement and evaluate this approach in the role-playing game of Angband.