AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

We provide a user guide on the analysis of data (including best–worst and best–best data) generated from discrete-choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post-estimation. We also provide a review of standard software. In providing this guide, we endeavour to not only provide guidance on choice modelling but to do so in a way that provides a ‘way in’ for researchers to the practicalities of data analysis. We argue that choice of modelling approach depends on the research questions, study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful to researchers not only within but also beyond health economics

Detecting drinking episodes in young adults using smartphone-based sensors

Abstract Alcohol use in young adults is common, with high rates of morbidity and mortality largely due to periodic, heavy drinking episodes (HDEs). Behavioral interventions delivered through electronic communication modalities (e.g., text messaging) can reduce the frequency of HDEs in young adults, but effects are small. One way to amplify these effects is to deliver support materials proximal to drinking occasions, but this requires knowledge of when they will occur. Mobile phones have built-in sensors that can potentially be useful in monitoring behavioral patterns associated with the initiation of drinking occasions. The objective of our work is to explore the detection of daily-life behavioral markers using mobile phone sensors and their utility in identifying drinking occasions. We utilized data from 30 young adults aged 21–28 with past hazardous drinking and collected mobile phone sensor data and daily Experience Sampling Method (ESM) of drinking for 28 consecutive days. We built a machine learning-based model that is 96.6% accurate at identifying non-drinking, drinking and heavy drinking episodes. We highlight the most important features for detecting drinking episodes and identify the amount of historical data needed for accurate detection. Our results suggest that mobile phone sensors can be used for automated, continuous monitoring of at-risk populations to detect drinking episodes and support the delivery of timely interventions

Challenges of quantified-self:encouraging self-reported data logging during recurrent smartphone usage

Abstract We argue that improved data entry can motivate Quantified-Self (QS) users to better engage with QS applications. To improve data entry, we investigate the notion of transforming active smartphone usage into data logging contributions through alert dialogs. We evaluate this assertion in a 4-week long deployment with 48 participants. We collect 17,906 data entries, where 68.3% of the entries are reported using the alert dialogs. We demonstrate that QS applications can benefit from alert dialogs: to increase data precision, frequency, and reduce the probability of forgetfulness in data logging. We investigate the impact of usage session type (e.g., sessions with different goals or durations) and the assigned reminder delay on frequency of data contributions. We conclude with insights gathered from our investigation, and the implications they have on future designs

Economias de escala na produção de leite: uma análise dos estados de Rondônia, Tocantins e Rio de Janeiro

Neste trabalho analisou-se o problema da manutenção, no longo prazo, dos produtores de leite na atividade. Foram analisados estabelecimentos nos estados de Rondônia, Tocantins e Rio de Janeiro. O objetivo do trabalho foi verificar a existência de economias de escala entre os produtores de leite. A função que apresentou melhor aderência aos dados foi a de custo translog. Os fatores de produção considerados foram: capital, terra, trabalho e custeio. A análise econômica mostra a dificuldade de sobrevivência dos estabelecimentos no longo prazo. Isto ocorre porque a relação capital imobilizado/produção é muito alta. Os resultados da regressão revelam que a grande maioria dos produtores da amostra está na faixa de economias de escala, sendo que apenas 3,4% destes estão na faixa de deseconomias de escala. O ponto de custo médio mínimo foi obtido com média diária de 487 litros por dia. Outro grupo composto por 10% da amostra está mais próximo do ponto de custo médio mínimo e apresenta produção diária entre 183 e 487 litros por dia. E finalmente, o último grupo apresentando uma produção inferior a 180 litros/dia. Este grupo pode reduzir de forma significativa os custos aumentando a produção e, usufruindo assim das economias de escala.<br>This study aimed to analize the business maintenance issue faced by milk producers, in the long term. It was analized farms in the states of Rondônia, Tocantins and Rio de Janeiro. The main goal of this study was to verify the existence of economies of scale among milk producers. The function that presented better adequacy to the data was that of cost translog. The production factors taken into consideration were: capital, land, work and direct expenses. The economic analysis shows the difficulties to survive faced by farmers in the long term. This happens because the relation immobilized capital/production is very high. The regression results reveal that the vast majority of producers in this case study operate in economies of scale, being that only 3.4% of them do not. The point of minimum average cost was obtained at about 178 thousand liters per year, that is, an average of 487 liters per day. Another group formed by 10% of the case study is closer to the point of minimum average cost and presents a daily production between 183 and 487 liters. And finally, the last group which presents itself in a less favorable situation with a production lower than 180 liters per day. It is highlighted that these producers can significantly reduce their costs if they increase their production, once they are found in the most accentuated part of the average cost curve in the long term and can greatly develop in the activity by using the economies of scale