Learning Invariant Molecular Representation in Latent Discrete Space

Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when data for training and testing originate from different environments. To address this issue, we propose a new framework for learning molecular representations that exhibit invariance and robustness against distribution shifts. Specifically, we propose a strategy called ``first-encoding-then-separation'' to identify invariant molecule features in the latent space, which deviates from conventional practices. Prior to the separation step, we introduce a residual vector quantization module that mitigates the over-fitting to training data distributions while preserving the expressivity of encoders. Furthermore, we design a task-agnostic self-supervised learning objective to encourage precise invariance identification, which enables our method widely applicable to a variety of tasks, such as regression and multi-label classification. Extensive experiments on 18 real-world molecular datasets demonstrate that our model achieves stronger generalization against state-of-the-art baselines in the presence of various distribution shifts. Our code is available at https://github.com/HICAI-ZJU/iMoLD

Effects of different proteases enzymatic extraction on the lipid yield and quality of Antarctic krill oil

This study was investigated the effects of six proteases (papain, compound proteinase, acidic protease, neutrase, pancreatin, and alcalase) on the lipid yield and quality of krill oil. The result shown that the krill oil extracted by alcalase and compound proteinase led to comparatively higher lipid yields (5.29% and 4.90%, respectively), Content of tocopherols and vitamin A, the content of omega‐3 polyunsaturated fatty acids (PUFAs) and phospholipids extracted by alcalase was relatively higher. Control and alcalase had comparatively higher concentration of astaxanthin. On the whole, compared with the extraction of solvent, enzymatic hydrolysis could improve the quality and the lipid yield of krill oil. Therefore, enzymatic hydrolysis could be used as a better method to extract krill oil

DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations

AI-aided drug discovery (AIDD) is gaining popularity due to its potential to make the search for new pharmaceuticals faster, less expensive, and more effective. Despite its extensive use in numerous fields (e.g., ADMET prediction, virtual screening), little research has been conducted on the out-of-distribution (OOD) learning problem with noise. We present DrugOOD, a systematic OOD dataset curator and benchmark for AIDD. Particularly, we focus on the drug-target binding affinity prediction problem, which involves both macromolecule (protein target) and small-molecule (drug compound). DrugOOD offers an automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise level annotations, and rigorous benchmarking of SOTA OOD algorithms, as opposed to only providing fixed datasets. Since the molecular data is often modeled as irregular graphs using graph neural network (GNN) backbones, DrugOOD also serves as a valuable testbed for graph OOD learning problems. Extensive empirical studies have revealed a significant performance gap between in-distribution and out-of-distribution experiments, emphasizing the need for the development of more effective schemes that permit OOD generalization under noise for AIDD

Thin film composite membranes functionalized with montmorillonite and hydrotalcite nanosheets for CO<inf>2</inf>/N<inf>2</inf>separation

© 2017 Elsevier B.V. Montmorillonite (MMT) and Mg-Al hydrotalcite (HT) nanosheets were prepared via vigorous agitation and ultrasonic, respectively. In order to increase the permeability of CO2, poly (PEA-MMT-TMC)/PS and poly (PEA-HT-TMC)/PS composite membranes were prepared via interfacial polymerization by adding the dimensional (2D) inorganic nanosheets (MMT and HT) into the aqueous phase of PEA. And in consequence, the poly (PEA-MMT-TMC)/PS composite membrane showed CO2permeability of 15.87 barrer and the CO2/N2selectivity of 37 at 1.0 bar when the MMT concentration was 0.068 wt%. The poly (PEA-HT-TMC)/PS composite membrane also showed CO2permeability of 15.3 barrer and the CO2/N2selectivity of 40 at 1.0 bar when the HT concentration was 0.25 wt%. Compared with the controlled membrane (CO2permeability: 6.9 barrer, CO2/N2selectivity: 103), the CO2permeability increased after incorporating the inorganic nanosheets into the membranes and maintained the pretty CO2/N2selectivity. The addition of exfoliated MMT and HT could facilitate the gas permeation to improve the gas separation performance of the composite membranes. Also, the combination of inorganic nanosheets with organic membrane has a great potential application in the gas separation

Zwitterionic functionalized layered double hydroxides nanosheets for a novel charged mosaic membrane with high salt permeability

© 2016 Elsevier B.V.. Charged mosaic membranes containing equivalent cationic and anionic exchange capacities are capable of decreasing the Donnan effect and thus accelerating salts permeation, while maintaining a high rejection of low molecular weight organics. In this study, charged nanosheets zwitterion-hydrotalcite (ZHT) was synthesized by grafting sulfobetaine methacrylate (SBMA) on the surface of positively charged Mg/Al hydrotalcite via surface initiated reverse atom transfer radical polymerization (RATRP). Subsequently, charged mosaic membranes were prepared by embedding different amounts of zwitterion-hydrotalcite into polyethersulfone (PES) casting solution via non-solvent induced phase separation (NIPS). Fourier transforms infrared spectra (FT-IR) and transmission electron microscopy (TEM) indicates that the zwitterion-hydrotalcite was successfully synthesized and well exfoliated. X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), ionic exchange capacity (IEC), surface zeta potential measurement and water contact angle were employed to investigate the effect of ZHT content on overall performance of prepared membranes. It was found that charged mosaic membranes manifested an enhanced ionic exchange capacity, surface hydrophilicity and hydraulic permeability compared to original membrane. Importantly, the charged mosaic membranes presented excellent dyes retention (86.7% for Reactive Red 49), superior salt permeation, and high water flux (80.2 L m-2h-1) under 0.4 MPa. Furthermore, the retention of MgCl2, Na2SO4and NaCl was as low as 9.3%, 7.6% and 0.53%, respectively. It is worth noting that ultra-high salt permeation as a bright spot of charged mosaic membranes could be achieved, which was ascribed to the introduction of zwitterion-hydrotalcite. A mechanism of salt transport through charged mosaic membranes is proposed in this study. Overall, these promising results demonstrate the potential of charged mosaic membranes and suggest their comfortable use in dyes separation