Mol. Pharmaceutics 2024, 21, 9, 4576–4588
期刊：Molecular Pharmaceutics
作者：Michael A. Bellucci, Lina Yuan, Grahame R. Woollam, Bing Wang, Liwen Fang, Yunfei Zhou, Chandler Greenwell, Sivakumar Sekharan, Xiaolan Ling*, GuangXu Sun*
時間：2024-08-20

The use of different template surfaces in crystallization experiments can directly influence the nucleation kinetics, crystal growth, and morphology of active pharmaceutical ingredients (APIs). Consequently, templated nucleation is an attractive approach to enhance crystal nucleation kinetics and preferentially nucleate desired crystal polymorphs for solid-form drug molecules, particularly large and flexible molecules that are difficult to crystallize. Herein, we investigate the effect of polymer templates on the crystal nucleation of clotrimazole and ketoprofen with both experiments and computational methods. Crystallization was carriedout in toluene solvent for both APIs with a template library consisting of 12 different polymers. In complement to the experimental studies, we developed a computational workflow based on molecular dynamics (MD) and derived descriptors from the simulations to score and rank API–polymer interactions. The descriptors were used to measure the energy of interaction (EOI), hydrogen bonding, and rugosity (surface roughness) similarity between the APIs and polymer templates. We used a variety of machine learning models (14 in total) along with these descriptors to predict the crystallization outcome of the polymer templates. We found that simply rank-ordering the polymers by their API–polymer interaction energy descriptors yielded 92% accuracy in predicting the experimental outcome for clotrimazole and ketoprofen. The most accurate machine learning model for both APIs was found to be a random forest model. Using these models, we were able to predict the crystallization outcomes for all polymers. Additionally, we have performed a feature importance analysis using the trained models and found that the most predictive features are the energy descriptors. These results demonstrate that API–polymer interaction energies are correlated with heterogeneous crystallization outcomes.

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Mol. Pharmaceutics 2024, 21, 8, 3800–3814
期刊：Molecular Pharmaceutics
作者：Maryam Sadat Sadeghi*, Rui Guo, Michael A. Bellucci, Jaypee Quino, Erika L. Buckle, Matthew L. Nisbet, Zhuocen Yang*, Chandler Greenwell, Danielle E. Gorka, Frank C. Pickard IV, Geoffrey P. F. Wood, Guangxu Sun, Shu-Hao Wen, Joseph F. Krzyzaniak, Paul A. Meenan, Bruno C. Hancock, Xiaojing Helen Yang*
時間：2024-07-25

Two anhydrous polymorphs of the novel antiviral medicine nirmatrelvir were discovered during the development of Paxlovid, Pfizer’s oral Covid-19 treatment. A comprehensive experimental and computational approach was necessary to distinguish the two closely related polymorphs, herein identified as Forms 1 and 4. This approach paired experimental methods, including powder X-ray diffraction and single-crystal X-ray diffraction, solid-state experimental methods, thermal analysis, solid-state nuclear magnetic resonance and Raman spectroscopy with computational investigations comprising crystal structure prediction, Gibbs free energy calculations, and molecular dynamics simulations of the polymorphic transition. Forms 1 and 4 were ultimately determined to be enantiotropically related polymorphs with Form 1 being the stable form above the transition temperature of ～17 °C and designated as the nominated form for drug development. The work described in this paper shows the importance of using highly specialized orthogonal approaches to elucidate the subtle differences in structure and properties of similar solid-state forms. This synergistic approach allowed for unprecedented speed in bringing Paxlovid to patients in record time amidst the pandemic.

The post Tale of Two Polymorphs: Investigating the Structural Differences and Dynamic Relationship between Nirmatrelvir Solid Forms (Paxlovid) appeared first on 晶泰科技 XtalPi.

期刊：Computational and Structural Biotechnology Journal Volume 23, December 2024, Pages 2122-2131

作者：Xiaodong Wang, Xiangrui Gao, Xuezhe Fan, Zhe Huai, Genwei Zhang, Mengcheng Yao, Tianyuan Wang, Xiaolu Huang, Lipeng Lai

時間：2024.05.16

B-cell epitope identification plays a vital role in the development of vaccines, therapies, and diagnostic tools. Currently,?molecular docking?tools in B-cell epitope prediction are heavily influenced by empirical parameters and require significant computational resources, rendering a great challenge to meet large-scale prediction demands. When predicting epitopes from antigen-antibody complex, current artificial intelligence algorithms cannot accurately implement the prediction due to insufficient protein feature representations, indicating novel algorithm is desperately needed for efficient protein information extraction. In this paper, we introduce a multimodal model called WUREN (Whole-modal Union Representation for Epitope predictioN), which effectively combines sequence, graph, and structural features. It achieved AUC-PR scores of 0.213 and 0.193 on the solved structures and AlphaFold-generated structures, respectively, for the independent test proteins selected from DiscoTope3 benchmark. Our findings indicate that WUREN is an efficient feature extraction model for?protein complexes, with the generalizable application potential in the development of protein-based drugs. Moreover, the streamlined framework of WUREN could be readily extended to model similar biomolecules, such as?nucleic acids, carbohydrates, and?lipids.

The post WUREN: Whole-modal union representation for epitope prediction appeared first on 晶泰科技 XtalPi.

期刊：Nature Communications?volume?15, Article?number:?3985?(2024)

作者：Wanbiao Chen,?Rongfeng Zou,?Yi Mei,?Jiawei Li,?Yumi Xuan,?Bing Cui,?Junjie Zou,?Juncheng Wang,?Shaoquan Lin,?Zhe Zhang?&?Chongyuan Wang

時間：2024.05.11

Pentamidine and melarsoprol are primary drugs used to treat the lethal human sleeping sickness caused by the parasite?Trypanosoma brucei. Cross-resistance to these two drugs has recently been linked to aquaglyceroporin 2 of the trypanosome (TbAQP2). TbAQP2 is the first member of the aquaporin family described as capable of drug transport; however, the underlying mechanism remains unclear. Here, we present cryo-electron microscopy structures of TbAQP2 bound to pentamidine or melarsoprol. Our structural studies, together with the molecular dynamic simulations, reveal the mechanisms shaping substrate specificity and drug permeation. Multiple amino acids in TbAQP2, near the extracellular entrance and inside the pore, create an expanded conducting tunnel, sterically and energetically allowing the permeation of pentamidine and melarsoprol. Our study elucidates the mechanism of drug transport by TbAQP2, providing valuable insights to inform the design of drugs against trypanosomiasis.

The post Structural insights into drug transport by an aquaglyceroporin appeared first on 晶泰科技 XtalPi.

期刊：Cancer Res?(2024) 84 (6_Supplement): 3525.

作者：Genwei Zhang; Jiewen Du; Xiangrui Gao; Tianyuan Wang; Zhenghui Wang; Qingxia Zhang; Tongren Liu; Dong Chen; Ruohan Zhu; Yalong Zhao; Chi Han Samson Li; Melvin Toh; Lipeng Lai

時間：2024.04.05

Background: The advent of immune checkpoint inhibitors has improved morbidity and mortality for some cancers, and recent breakthroughs in gene & cell therapy have shed light on curing some types of blood cancers. However, many cancers remain intractable and the development of novel, effective and safe therapies continue to be a priority. Cancer vaccines as a cancer immunotherapy approach has seen a resurgence in recent years, due to the success of mRNA vaccines for the COVID-19. However, the accurate prediction of immunogenicity of cancer vaccines remains elusive.

Methods: Our models predict the probability of a given peptide derived from the protein of interest to be presented by MHC-I or MHC-II. For MHC-I antigen presentation model development, over 17 million entries in the dataset were collected from published literature and available databases, e.g., IEDB, with peptide lengths ranging from 8 to 11. The peptides were restricted to 150 unique MHC-I alleles. Similarly, ~4 million entries with peptide lengths ranging from 13 to 21 were collected for MHC-II antigen presentation model development, and the peptides were restricted to 19 unique MHC-II alleles. To develop advanced antigen presentation models, a language model was chosen as the backbone network and contrast learning was used to better discriminate the peptide-MHC match versus mismatch. Overall, both MHC-I and MHC-II presentation models were constructed with about 30 million parameters. To validate the model prediction accuracy, automated peptide synthesis and surface plasmon resonance (SPR) technologies were applied.

Results: Using open-sourced data, our developed AI models surpassed the performance of state-of-the-art prediction algorithms, the latest versions of NetMHCpan and MixMHCpred, for both MHC-I and MHC-II antigen presentation. Furthermore, to validate the algorithm accuracy and the peptide immunogenicity, 28 predicted patentable peptides derived from mutated TP53 protein were synthesized and their binding to respective common HLA alleles were validated using SPR. We found that greater than 80% of the peptides display binding affinities that are stronger than the positive control, suggesting that AI significantly improves neoantigen peptide vaccine design.

Conclusions: We developed advanced AI algorithms to rapidly design shared neoantigen T cell epitopes with predicted strong binding affinity to MHC-I and MHC-II. We envision that the epitopes predicted and designed by our AI algorithms possess great potential in advancing the field of off-the-shelf cancer vaccine development and hold the promise of significantly benefiting patients, once translated into the clinic.

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期刊：Methods Volume 222,?February 2024, Pages 112-121

作者：Lianjun?Zheng,?Fangjun?Shi,?Chunwang?Peng,?Min?Xu,?Fangda?Fan,?Yuanpeng?Li,?Lin?Zhang,?Jiewen?Du,?Zonghu?Wang,?Zhixiong?Lin,?Yina?Sun,?Chenglong?Deng,?Xinli?Duan,?Lin?Wei,?Chuanfang?Zhao,?Lei?Fang,?Peiyu?Zhang,?Songling?Ma,?Lipeng?Lai,?Mingjun?Yang?

時間：2024.01.11

Design of molecules for candidate compound selection is one of the central challenges in drug discovery due to the complexity of chemical space and requirement of multi-parameter optimization. Here we present an application scenario-oriented platform (ID4Idea) for molecule generation in different scenarios of drug discovery. This platform utilizes both library or rule based and generative based algorithms (VAE,?RNN, GAN, etc.), in combination with various AI learning types (pre-training, transfer learning, reinforcement learning, active learning, etc.) and input representations (1D SMILES, 2D graph, 3D shape,?binding site,?pharmacophore, etc.), to enable customized solutions for a given molecular design scenario. Besides the usual generation followed screening protocol, goal-directed molecule generation can also be conducted towards predefined goals, enhancing the efficiency of hit identification, lead finding, and lead optimization. We demonstrate the effectiveness of ID4Idea platform through case studies, showcasing customized solutions for different design tasks using various input information, such as binding pockets, pharmacophores, and compound representations. In addition, remaining challenges are discussed to unlock the full potential of AI models in drug discovery and pave the way for the development of novel therapeutics.

The post Application scenario-oriented molecule generation platform developed for drug discovery appeared first on 晶泰科技 XtalPi.

期刊：Journal of Chemical Theory and Computation?2024, 20, 2, 799–818

作者：Bai Xue, Qingyi Yang, Qiaochu Zhang, Xiao Wan, Dong Fang, Xiaolu Lin, Guangxu Sun, Gianpaolo Gobbo, Fenglei Cao, Alan M. Mathiowetz, Benjamin J. Burke, Robert A. Kumpf, Brajesh K. Rai, Geoffrey P. F. Wood, Frank C. Pickard IV, Junmei Wang, Peiyu Zhang, Jian Ma, Yide Alan Jiang, Shuhao Wen, Xinjun Hou*, Junjie Zou*, Mingjun Yang*

時間：2023.12.29

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein–ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG?and 0.92 kcal/mol for ΔG?on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.

The post Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation appeared first on 晶泰科技 XtalPi.

期刊：Journal of Chemical Information and Modeling?2023, 63, 16, 5341–5355

作者：Lin Wei, Min Xu, Zhiqiang Liu, Chongguo Jiang, Xiaohua Lin, Yaogang Hu, Xiaoming Wen, Rongfeng Zou, Chunwang Peng, Hongrui Lin, Guo Wang, Lijun Yang, Lei Fang*, Mingjun Yang, Peiyu Zhang

時間：2023.08.07

Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the?ID4Inno?drug discovery platform, featuring innovative artificial intelligence, high-accuracy computational chemistry, and high-performance cloud computing. The workflow was validated by discovering a few potent hit compounds (best IC₅₀?is ～0.80 μM) against PI5P4K-β, a novel anti-cancer target. Furthermore, by applying the tools implemented in?ID4Inno, we managed to optimize these hit compounds and finally obtained five hit series with different scaffolds, all of which showed high activity against PI5P4K-β. These results demonstrate the effectiveness of?ID4inno?in driving hit identification based on artificial intelligence, computational chemistry, and cloud computing.

The post Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study appeared first on 晶泰科技 XtalPi.

Mol. Pharmaceutics 2023, 20, 7, 3380–3392
期刊：Molecular Pharmaceutics
作者：Yuriy A. Abramov, Luca Iuzzolino, Yingdi Jin, Gregory York, Chien-Hung Chen, C. Scott Shultz, Zhuocen Yang, Chao Chang, Baimei Shi, Tian Zhou, Chandler Greenwell, Sivakumar Sekharan*, Alfred Y. Lee*
時間：2023-06-06

Crystal structure prediction (CSP) is an invaluable tool in the pharmaceutical industry because it allows to predict all the possible crystalline solid forms of small-molecule active pharmaceutical ingredients. We have used a CSP-based cocrystal prediction method to rank ten potential cocrystal coformers by the energy of the cocrystallization reaction with an antiviral drug candidate, MK-8876, and a triol process intermediate, 2-ethynylglyclerol. For MK-8876, the CSP-based cocrystal prediction was performed retrospectively and successfully predicted the maleic acid cocrystal as the most likely cocrystal to be observed. The triol is known to form two different cocrystals with 1,4-diazabicyclo[2.2.2]octane (DABCO), but a larger solid form landscape was desired. CSP-based cocrystal screening predicted the triol-DABCO cocrystal as rank one, while a triol-l-proline cocrystal was predicted as rank two. Computational finite-temperature corrections enabled determination of relative crystallization propensities of the triol-DABCO cocrystals with different stoichiometries and prediction of the triol-l-proline polymorphs in the free-energy landscape. The triol-l-proline cocrystal was obtained during subsequent targeted cocrystallization experiments and was found to exhibit an improved melting point and deliquescence behavior over the triol-free acid, which could be considered as an alternative solid form in the synthesis of islatravir.

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Small Methods, Volume 7,?Issue 6, June 20, 2023, 2201692
期刊：Small Methods
作者：Michael A. Bellucci,?Anke Marx,?Bing Wang,?Liwen Fang,?Yunfei Zhou,?Chandler Greenwell,?Zhuhong Li,?Axel Becker,?GuangXu Sun,?Jan Gerit Brandenburg,?Sivakumar Sekharan
時間：2023-03-25

The crystal habit can have a profound influence on the physical properties of crystalline materials, and thus controlling the crystal morphology is of great practical relevance across many industries. Herein, this work investigates the effect of polymer additives on the crystal habit of metformin HCl with both experiments and computational methods with the aim of developing a combined screening approach for crystal morphology engineering. Crystallization experiments of metformin HCl are conducted in methanol and in an isopropanol-water mixture (8:2?V/V). Polyethylene glycol, polyvinylpyrrolidone, Tween80, and hydroxypropyl methylcellulose polymer additives are used in low concentrations (1–2% w/w) in the experiments to study the effect they have on modifying the crystal habit. Additionally, this work has developed computational methods to characterize the morphology “l(fā)andscape” and quantifies the overall effect of solvent and additives on the predicted crystal habits. Further analysis of the molecular dynamics simulations is used to rationalize the effect of additives on specific crystal faces. This work demonstrates that the effects of additives on the crystal habit are a result of their absorption and interactions with the slow growing {100} and {020} faces.

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