An open unified deep graph learning framework for discovering drug leads

Computational discovery of ideal lead compounds is a critical process for modern drug discovery. It comprises multiple stages: hit screening, molecular property prediction, and molecule optimization. Current efforts are disparate, involving the establishment of models for each stage, followed by multi-stage multi-model integration. However, this is non-ideal, as clumsy integration of incompatible models increases research overheads, and may even reduce success rates in drug discovery. Facilitating compatibilities requires establishing inherent model consistencies across lead discovery stages. Towards that effect, we propose an open deep graph learning (DGL) based pipeline: generative adversarial feature subspace enhancement (GAFSE), which first unifies the modeling of these stages into one learning framework. GAFSE also offers standardized modular design and streamlined interfaces for future expansions and community support. GAFSE combines adversarial/generative learning, graph attention network, graph reconstruction network, and optimizes the classification/regression loss, adversarial/generative loss, and reconstruction loss simultaneously. Convergence analysis theoretically guarantees model generalization performance. Exhaustive benchmarking demonstrates that the GAFSE pipeline achieves excellent performance across almost all lead discovery stages, while also providing valuable model interpretability. Hence, we believe this tool will enhance the efficiency and productivity of drug discovery researchers.Comment: This article is used as the preliminary studies for the application of Lee Kuan Yew Postdoctoral Fellowship (LKYPDF) 2023 in Singapore. All rights reserve

Deforestation, forest degradation and readiness of local people of Lubuk Antu, Sarawak for REDD+

Reducing emissions from deforestation and forest degradation-plus (REDD+) is considered as an important mitigation strategy against global warming. However, the implementation of REDD+ can adversely affect local people who have been practicing shifting cultivation for generations. We analyzed Landsat-5 Thematic Mapper images of 1990 and 2009 to quantifying deforestation and forest degradation at Lubuk Antu District, a typical rural area of Sarawak, Malaysia. The results showed significant loss of intact forest at 0.9% per year, which was substantially higher than the rate of Sarawak. There were increases of oil palm and rubber areas but degraded forest, the second largest land cover type, had increased considerably. The local people were mostly shifting cultivators, who indicated readiness of accepting the REDD+ mechanism if they were given compensation. We estimated the monthly willingness to accept (WTA) at RM462, which can be considered as the opportunity cost of foregoing their existing shifting cultivation. The monthly WTA was well correlated with their monthly household expenses. Instead of cash payment, rubber cultivation scheme was the most preferred form of compensation

Modeling the influence of attitudes, trust, and beliefs on endoscopists’ acceptance of artificial intelligence applications in medical practice

IntroductionThe potential for deployment of Artificial Intelligence (AI) technologies in various fields of medicine is vast, yet acceptance of AI amongst clinicians has been patchy. This research therefore examines the role of antecedents, namely trust, attitude, and beliefs in driving AI acceptance in clinical practice.MethodsWe utilized online surveys to gather data from clinicians in the field of gastroenterology.ResultsA total of 164 participants responded to the survey. Participants had a mean age of 44.49 (SD = 9.65). Most participants were male (n = 116, 70.30%) and specialized in gastroenterology (n = 153, 92.73%). Based on the results collected, we proposed and tested a model of AI acceptance in medical practice. Our findings showed that while the proposed drivers had a positive impact on AI tools’ acceptance, not all effects were direct. Trust and belief were found to fully mediate the effects of attitude on AI acceptance by clinicians.DiscussionThe role of trust and beliefs as primary mediators of the acceptance of AI in medical practice suggest that these should be areas of focus in AI education, engagement and training. This has implications for how AI systems can gain greater clinician acceptance to engender greater trust and adoption amongst public health systems and professional networks which in turn would impact how populations interface with AI. Implications for policy and practice, as well as future research in this nascent field, are discussed

Protocol of a systematic review and network meta-analysis for the prevention and treatment of perinatal depression

Introduction Perinatal depression is common and can often lead to adverse health outcomes for mother and child. Multiple pharmacological and non-pharmacological treatments have been evaluated against usual care or placebo controls in meta-analyses for preventing and treating perinatal depression compared. It is not yet established which of these candidate treatments might be the optimal approach for prevention or treatment. Methods and analysis A systematic review and Bayesian network meta-analyses will be conducted. Eight electronic databases shall be searched for randomised controlled trials that have evaluated the effectiveness of treatments for prevention and/or treatment of perinatal depression. Screening of articles shall be conducted by two reviewers independently. One network meta-analysis shall evaluate the effectiveness of interventions in preventing depression during the perinatal period. A second network meta-analysis shall compare the effectiveness of treatments for depression symptoms in women with perinatal depression. Bayesian 95% credible intervals shall be used to estimate the pooled mean effect size of each treatment, and surface under cumulative ranking area will be used to rank the treatments\u27 effectiveness. Ethics and dissemination We shall report our findings so that healthcare providers can make informed decisions on what might be the optimal approach for addressing perinatal depression to prevent cases and improve outcomes in those suffering from depression through knowledge exchange workshops, international conference presentations and journal article publications. PROSPERO registration number CRD42020200081

5-deazaflavin derivatives as inhibitors of p53 ubiquitination by HDM2

Based on previous reports of certain 5-deazaflavin derivatives being capable of activating the tumour suppressor p53 in cancer cells through inhibition of the p53-specific ubiquitin E3 ligase HDM2, we have conducted an structure–activity relationship (SAR) analysis through systematic modification of the 5-deazaflavin template. This analysis shows that HDM2-inhibitory activity depends on a combination of factors. The most active compounds (e.g., 15) contain a trifluoromethyl or chloro substituent at the deazaflavin C9 position and this activity depends to a large extent on the presence of at least one additional halogen or methyl substituent of the phenyl group at N10. Our SAR results, in combination with the HDM2 RING domain receptor recognition model we present, form the basis for the design of drug-like and potent activators of p53 for potential cancer therapy