What blocks more anticancer platinum complexes from experiment to clinic: Major problems and potential strategies from drug design perspectives

2021 ◽  
Vol 449 ◽  
pp. 214210
Author(s):  
Kun Peng ◽  
Bing-Bing Liang ◽  
Wenting Liu ◽  
Zong-Wan Mao
2020 ◽  
Author(s):  
Yuyao Yang ◽  
Shuangjia Zheng ◽  
Shimin Su ◽  
Jun Xu ◽  
Hongming Chen

Fragment based drug design represents a promising drug discovery paradigm complimentary to the traditional HTS based lead generation strategy. How to link fragment structures to increase compound affinity is remaining a challenge task in this paradigm. Hereby a novel deep generative model (AutoLinker) for linking fragments is developed with the potential for applying in the fragment-based lead generation scenario. The state-of-the-art transformer architecture was employed to learn the linker grammar and generate novel linker. Our results show that, given starting fragments and user customized linker constraints, our AutoLinker model can design abundant drug-like molecules fulfilling these constraints and its performance was superior to other reference models. Moreover, several examples were showcased that AutoLinker can be useful tools for carrying out drug design tasks such as fragment linking, lead optimization and scaffold hopping.


2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.


Author(s):  
Koji INAKA ◽  
Saori ICHIMIZU ◽  
Izumi YOSHIZAKI ◽  
Kiyohito KIHIRA ◽  
Elena G. LAVRENKO ◽  
...  

A series of space experiments aboard the International Space Station (ISS) associated with high-quality Protein Crystal Growth (PCG) in microgravity conditions can be considered as a unique and one of the best examples of fruitful collaboration between Japanese and Russian scientists and engineers in space, which includes also other ISS International Partners. X-ray diffraction is still the most powerful tool to determine the protein three dimensional structure necessary for Structure based drug design (SBDD). The major purpose of the experiment is to grow high quality protein crystals in microgravity for X-ray diffraction on Earth. Within one and a half decade, Japan and Russia have established an efficient process over PCG in space to support latest developments over drug design and structural biology. One of the keys for success of the experiment lies in how precisely pre-launch preparations are made. Japanese party provides flight equipment for crystallization and ensures the required environment to support the experiment aboard of the ISS’s Kibo module, and also mainly takes part of the experiment ground support such as protein sample characterization, purification, crystallization screening, and solution optimization for microgravity experiment. Russian party is responsible for integration of the flight items equipped with proteins and precipitants on board Russian transportation space vehicles (Soyuz or Progress), for delivery them at the ISS, transfer to Kibo module, and returning the experiments’ results back on Earth aboard Soyuz manned capsule. Due to close cooperation of the parties and solid organizational structure, samples can be launched at the ISS every half a year if the ground preparation goes smoothly. The samples are crystallized using counter diffusion method at 20 degree C for 1–2.5 months. After samples return, the crystals are carefully taken out from the capillary, and frozen for X-ray diffraction at SPring8 facility in Japan. Extensive support of researchers from both countries is also a part of this process. The paper analyses details of the PCG experiment scheme, unique and reliable technology of its execution, and contains examples of the application. Key words: International Space Station, Protein crystals, Microgravity, International collaboration.


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