LOGISTICS PLATFORMS - TRENDS AND CHALLENGES

Logistics platforms (LP) are business models developed to improve the performance of all logistics activities of a supply chain (SC). About logistics platforms, the scientific literature details the management, implementation, importance, typologies, comparisons with international platforms, as well as cited case studies therein. The literature also highlights many trends of the adoption of technology as well as challenges resulting from the rapid evolution of said technology. We present a discussion of an LP, as well as an LP’s importance to its SC. We discuss eight types of LPs, their applications, and their associated implementation phases. This important volume of articles that we summarize seeks to solve complex problems with mathematical formulations. The literature potentiates the processes carried out in LPs by means of case-study analyses through comparing some LPs of South America against the more technological-based and automation-based LPs of Europe, of Southeast Asia, and of North America. The studies of LPs in global SCs, and enclosed cycle SCs, have shown that there are many challenges stemming from global climate change, which places uncertainty in the process of estimating stochastic parameters in the new global market. This would mandate strengthening the methodologies of Hub- and Cross-docking and understanding trends, such as the need to fortify the management of LPs by utilizing information technologies and communication technologies and updating local markets to make global markets more resilient in the face of pending environmental shifts.

Modelos matemáticos para programação do sistema cross docking com múltiplas docas

Este trabalho foi motivado pela observação do elevado tempo de fluxo dos veículos nos centros de distribuição das empresas. Portanto, esse artigo se propõe a criar modelos que sequenciem de forma eficiente a entrada e saída de veículos do centro cross docking, minimizando o resultado de um dos principais indicadores nas organizações logísticas: o tempo de permanência dos veículos. Com o intuito de melhorar a eficiência computacional dos modelos propostos, realiza-se o estudo das variáveis e parâmetros envolvidos na programação linear inteira mista propondo opções para a redução do tempo de execução. Os modelos apresentados são baseados em variáveis de alocação considerando restrições de múltiplas docas. A experimentação computacional foi realizada por meio de um conjunto de 160 problemas-testes e os resultados evidenciaram que a diferença entre a quantidade de veículos de entrada e saída conseguem melhor explicar a proporcionalidade de resultados do que as atribuições de variáveis isoladas.

The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction

Structure-based drug design depends on the detailed knowledge of the three-dimensional (3D) structures of protein–ligand binding complexes, but accurate prediction of ligand-binding poses is still a major challenge for molecular docking due to deficiency of scoring functions (SFs) and ignorance of protein flexibility upon ligand binding. In this study, based on a cross-docking dataset dedicatedly constructed from the PDBbind database, we developed several XGBoost-trained classifiers to discriminate the near-native binding poses from decoys, and systematically assessed their performance with/without the involvement of the cross-docked poses in the training/test sets. The calculation results illustrate that using Extended Connectivity Interaction Features (ECIF), Vina energy terms and docking pose ranks as the features can achieve the best performance, according to the validation through the random splitting or refined-core splitting and the testing on the re-docked or cross-docked poses. Besides, it is found that, despite the significant decrease of the performance for the threefold clustered cross-validation, the inclusion of the Vina energy terms can effectively ensure the lower limit of the performance of the models and thus improve their generalization capability. Furthermore, our calculation results also highlight the importance of the incorporation of the cross-docked poses into the training of the SFs with wide application domain and high robustness for binding pose prediction. The source code and the newly-developed cross-docking datasets can be freely available at https://github.com/sc8668/ml_pose_prediction and https://zenodo.org/record/5525936, respectively, under an open-source license. We believe that our study may provide valuable guidance for the development and assessment of new machine learning-based SFs (MLSFs) for the predictions of protein–ligand binding poses.

First-in-Class Isonipecotamide-Based Thrombin and Cholinesterase Dual Inhibitors with Potential for Alzheimer Disease

Recently, the direct thrombin (thr) inhibitor dabigatran has proven to be beneficial in animal models of Alzheimer’s disease (AD). Aiming at discovering novel multimodal agents addressing thr and AD-related targets, a selection of previously and newly synthesized potent thr and factor Xa (fXa) inhibitors were virtually screened by the Multi-fingerprint Similarity Searching aLgorithm (MuSSeL) web server. The N-phenyl-1-(pyridin-4-yl)piperidine-4-carboxamide derivative 1, which has already been experimentally shown to inhibit thr with a Ki value of 6 nM, has been flagged by a new, upcoming release of MuSSeL as a binder of cholinesterase (ChE) isoforms (acetyl- and butyrylcholinesterase, AChE and BChE), as well as thr, fXa, and other enzymes and receptors. Interestingly, the inhibition potency of 1 was predicted by the MuSSeL platform to fall within the low-to-submicromolar range and this was confirmed by experimental Ki values, which were found equal to 0.058 and 6.95 μM for eeAChE and eqBChE, respectively. Thirty analogs of 1 were then assayed as inhibitors of thr, fXa, AChE, and BChE to increase our knowledge of their structure-activity relationships, while the molecular determinants responsible for the multiple activities towards the target enzymes were rationally investigated by molecular cross-docking screening.

From complete cross-docking to partners identification and binding sites predictions

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. We applied it to a few hundred of proteins, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.