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Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects. [ABSTRACT FROM AUTHOR]

Background Apolipoprotein E (APOE) [epsilon]4 is the single most important genetic risk factor for cognitive impairment and Alzheimer disease (AD), while lifestyle factors such as smoking, drinking, diet, and physical activity also have impact on cognition. The goal of the study is to investigate whether the association between lifestyle and cognition varies by APOE genotype among the oldest old. Methods and findings We used the cross-sectional data including 6,160 oldest old (aged 80 years old or older) from the genetic substudy of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) which is a national wide cohort study that began in 1998 with follow-up surveys every 2-3 years. Cognitive impairment was defined as a Mini-Mental State Examination (MMSE) score less than 18. Healthy lifestyle profile was classified into 3 groups by a composite measure including smoking, alcohol consumption, dietary pattern, physical activity, and body weight. APOE genotype was categorized as APOE [epsilon]4 carriers versus noncarriers. We examined the associations of cognitive impairment with lifestyle profile and APOE genotype using multivariable logistic regressions, controlling for age, sex, education, marital status, residence, disability, and numbers of chronic conditions. The mean age of our study sample was 90.1 (standard deviation [SD], 7.2) years (range 80-113); 57.6% were women, and 17.5% were APOE [epsilon]4 carriers. The mean MMSE score was 21.4 (SD: 9.2), and 25.0% had cognitive impairment. Compared with those with an unhealthy lifestyle, participants with intermediate and healthy lifestyle profiles were associated with 28% (95% confidence interval [CI]: 16%-38%, P < 0.001) and 55% (95% CI: 44%-64%, P < 0.001) lower adjusted odds of cognitive impairment. Carrying the APOE [epsilon]4 allele was associated with 17% higher odds (95% CI: 1%-31%, P = 0.042) of being cognitively impaired in the adjusted model. The association between lifestyle profiles and cognitive function did not vary significantly by APOE [epsilon]4 genotype (noncarriers: 0.47 [0.37-0.60] healthy versus unhealthy; carriers: 0.33 [0.18-0.58], P for interaction = 0.30). The main limitation was the lifestyle measurements were self-reported and were nonspecific. Generalizability of the findings is another limitation because the study sample was from the oldest old in China, with unique characteristics such as low body weight compared to populations in high-income countries. Conclusions In this study, we observed that healthier lifestyle was associated with better cognitive function among the oldest old regardless of APOE genotype. Our findings may inform the cognitive outlook for those oldest old with high genetic risk of cognitive impairment., Author(s): Xurui Jin 1,2, Wanying He 1, Yan Zhang 1, Enying Gong 3, Zhangming Niu 2, John Ji 4, Yaxi Li 1, Yi Zeng 5,6,7, Lijing L. Yan 1,5,8,9,10,* Introduction [...]

Biomedical knowledge graphs (KGs), which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods and non-uniform evaluation metrics. In this work, we established a comprehensive KG system for the biomedical field in an attempt to bridge the gap. Here, we introduced PharmKG, a multi-relational, attributed biomedical KG, composed of more than 500 000 individual interconnections between genes, drugs and diseases, with 29 relation types over a vocabulary of ~8000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure and disease word embedding, while preserving the semantic and biomedical features. For baselines, we offered nine state-of-the-art KG embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a KG in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical KG construction, embedding and application. [ABSTRACT FROM AUTHOR]

Finding target molecules with specific chemical properties plays a decisive role in drug development. We proposed GEOM-CVAE, a constrained variational autoencoder based on geometric representation for molecular generation with specific properties, which is protein-context-dependent. In terms of machine learning, it includes continuous feature embedding encoder and molecular generation decoder. Our key contribution is to propose an efficient geometric embedding method, including the spatial structure representations of drug molecule (converting the 3-D coordinates into image) and the geometric graph representations of protein target (modeling the protein surface as a mesh). The 3-D geometric information is vital to successful molecular generation, which is different from previous molecular generative methods based on 1-D or 2-D. Our model framework generates specific molecules in two phases, by first generating special image with molecular 3-D information to learn latent representations and generating molecules with constrained condition based on geometric graph convolution for specific protein and then inputting the generated structural molecules into a parser network for obtaining Simplified Molecular Input Line Entry System (SMILES) strings. Our model achieves competitive performance that implies its potential effectiveness to enable the exploration of the vast chemical space for drug discovery.

Nicotinamide adenine dinucleotide (NAD + ) is an important natural molecule involved in fundamental biological processes, including the TCA cycle, OXPHOS, β-oxidation, and is a co-factor for proteins promoting healthy longevity. NAD + depletion is associated with the hallmarks of ageing and may contribute to a wide range of age-related diseases including metabolic disorders, cancer, and neurodegenerative diseases. One of the central pathways by which NAD + promotes healthy ageing is through regulation of mitochondrial homeostasis via mitochondrial biogenesis and the clearance of damaged mitochondria via mitophagy. Here, we highlight the contribution of the NAD + -mitophagy axis to ageing and age-related diseases, and evaluate how boosting NAD + levels may emerge as a promising therapeutic strategy to counter ageing as well as neurodegenerative diseases including Alzheimer's disease. The potential use of artificial intelligence to understand the roles and molecular mechanisms of the NAD + -mitophagy axis in ageing is discussed, including possible applications in drug target identification and validation, compound screening and lead compound discovery, biomarker development, as well as efficacy and safety assessment. Advances in our understanding of the molecular and cellular roles of NAD + in mitophagy will lead to novel approaches for facilitating healthy mitochondrial homoeostasis that may serve as a promising therapeutic strategy to counter ageing-associated pathologies and/or accelerated ageing., (Copyright © 2019 Elsevier B.V. All rights reserved.)