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<i>Corrigendum to</i>: A holistic approach towards a generalizable machine learning predictor of cell penetrating peptides

The development of machine learning (ML) predictors does not necessarily require the employment of expansive classifiers and complex feature encoding schemes to achieve the highest accuracy scores. It rather requires data pre-processing, feature optimization, and robust evaluation to ensure consistent results and generalizability. Herein, we describe a multi-stage process to develop a reliable ML predictor of cell penetrating peptides (CPPs). We emphasize the challenges of: (i) the generation of representative datasets with all required pre-processing procedures; (ii) comprehensive and exclusive encoding of peptides using their amino acid composition; (iii) obtaining an optimized feature set using a simple classifier (support vector machine, SVM); (iv) ensuring consistent results; and (v) verifying generalizability at the highest achievable accuracy scores. Two peptide sub-spaces were used to generate the negative examples, which are required, along with the known CPPs, to train the classifier. These included: (i) randomly generated peptides with all amino acid types being equally represented and (ii) extracted peptides from receptor proteins. Results indicated that the randomly generated dataset performed perfectly well within its own peptide sub-space, while it poorly generalized to the other sub-space. Conversely, the dataset extracted from receptor proteins, while achieving lower accuracies, showed a perfect generalizability to the other peptide sub-space. We combined the qualities of these two datasets by utilizing the average of their predictions within our ultimate framework. This functional ML predictor, WLVCPP, and associated software and datasets can be downloaded from <a ext-link-type="uri" href="https://github.com/BahaaIsmail/WLVCPP">https://github.com/BahaaIsmail/WLVCPP</a>.

The effects of templates and seeds on the properties of nanosheet SAPO-34 molecular sieves and their catalytic performance in the MTO reaction

Nanoscale SAPO-34 molecular sieves were synthesized by adding different types of seed into hydrothermal synthesis systems with tetraethylammonium hydroxide (TEAOH) and triethylamine (TEA) & tetraethylammonium bromide (TEABr) as templates. The effects of different types of template and seed on the crystal structure, morphology, grain size and acidity of the molecular sieves were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), N2 isothermal adsorption–desorption and ammonia temperature-programmed desorption (NH3-TPD). The methanol-to-olefins (MTO) reaction performance of the synthesized samples was investigated in a fixed-bed reactor. The results showed that crystalline supernatant and seed soaking solution could be used as liquid seeds to assist in the synthesis of SAPO-34 molecular sieves with a lamellar structure. The yield of SAPO-34 synthesized by seed increased from 38.64 to 59.68%, and the methanol conversion rate was significantly improved as compared with that of SAPO-34 synthesized without seed. The nano-thickness of SAPO-34 synthesized with TEA&TEABr instead of TEAOH as template decreased from 100–150 to 40–50 nm, and the lifetime increased from 360 to 400 min with the original yield kept constant.