日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Targeting of interaction between BB0323-BB0238 informs new paradigms in Lyme disease therapeutics

靶向研究BB0323-BB0238之间的相互作用为莱姆病治疗提供了新的范式

Bista, Sandhya; Brangulis, Kalvis; Bhattachan, Bibek; Foor, Shelby D; Ronzetti, Michael H; Jain, Sankalp; Miller, Jenna; Subramanion, Jothy Lachumy; Kitsou, Chrysoula; Rana, Vipin S; Rai, Ganesha; Zakharov, Alexey V; Simeonov, Anton; Baljinnyam, Bolormaa; Pal, Utpal

AI-driven discovery of synergistic drug combinations against pancreatic cancer.

利用人工智能发现对抗胰腺癌的协同药物组合

Pourmousa Mohsen, Jain Sankalp, Barnaeva Elena, Jin Wengong, Hochuli Joshua, Itkin Zina, Maxfield Travis, Melo-Filho Cleber, Thieme Andrew, Wilson Kelli, Klumpp-Thomas Carleen, Michael Sam, Southall Noel, Jaakkola Tommi, Muratov Eugene N, Barzilay Regina, Tropsha Alexander, Ferrer Marc, Zakharov Alexey V

From Sequence to Response: AI-Guided Prediction of Nucleic Acid Nanoparticles Immune Recognitions.

从序列到响应:人工智能引导的核酸纳米颗粒免疫识别预测。

Johnson M Brittany, Jain Sankalp, McMillan Shea Jessica, Krueger Quinton, Doe Erwin, Miller Daniel, Pranger Katelynn, Hayth Hannah, Thornburgh Sable, Halman Justin, Khisamutdinov Emil F, Zakharov Alexey V, Afonin Kirill A

One size does not fit all: revising traditional paradigms for assessing accuracy of QSAR models used for virtual screening

一种方法并不适用于所有情况:修正用于虚拟筛选的QSAR模型准确性评估传统范式

Wellnitz, James; Jain, Sankalp; Hochuli, Joshua E; Maxfield, Travis; Muratov, Eugene N; Tropsha, Alexander; Zakharov, Alexey V

Editorial: Methods in predictive toxicology 2023

社论:2023 年预测毒理学方法

Jain, Sankalp; Manganelli, Serena; Gryshkova, Vitalina; Rodrigues, Maria Armanda; Magarkar, Aniket

Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data

基于Tox24挑战数据预测转甲状腺素蛋白结合亲和力的共识建模策略

Cirino, Thalita; Pinto, Luis; Iwan, Mateusz; Dougha, Alexis; Lučić, Bono; Kraljević, Antonija; Navoyan, Zaven; Tevosyan, Ani; Yeghiazaryan, Hrach; Khondkaryan, Lusine; Abelyan, Narek; Atoyan, Vahe; Babayan, Nelly; Iwashita, Yuma; Kimura, Kyosuke; Komasaka, Tomoya; Shishido, Koki; Nakamura, Taichi; Asada, Mizuho; Jain, Sankalp; Zakharov, Alexey V; Wang, Haobo; Liu, Wenjia; Chupakhin, Vladimir; Uesawa, Yoshihiro

Integrated Approach of Machine Learning and High-Throughput Screening to Identify Chemical Probe Candidates Targeting Aldehyde Dehydrogenases.

机器学习与高通量筛选相结合的方法来识别靶向醛脱氢酶的化学探针候选物。

Yasgar Adam, Jain Sankalp, Davies Marissa, Danchik Carina, Niehoff Taylor, Ran Jing, Rai Ganesha, Yang Shyh-Ming, Simeonov Anton, Zakharov Alexey V, Martinez Natalia J

Breaking the Phalanx: Overcoming Bacterial Drug Resistance with Quorum Sensing Inhibitors that Enhance Therapeutic Activity of Antibiotics

打破细菌耐药性的壁垒:利用群体感应抑制剂增强抗生素的治疗活性,克服细菌耐药性

Beasley, Jon-Michael; Dorjsuren, Dorjbal; Jain, Sankalp; Rath, Marielle; Scheufen Tieghi, Ricardo; Tropsha, Alexander; Simeonov, Anton; Zakharov, Alexey V; Muratov, Eugene

SCOPE: Revealing Hidden Mechanisms in Phenotypic Screens Through Target and Pathway Enrichment

SCOPE:通过靶点和通路富集揭示表型筛选中的隐藏机制

Kapoor, Abhijeet; Kelleher, Keith; Underhill, Suzanne; Jain, Sankalp; Harvey, Brandon K; Henderson, Mark J

Cell-Based Covalent-Capture Deubiquitinase Assay for Inhibitor Discovery.

用于抑制剂发现的基于细胞的共价捕获去泛素化酶测定

Doleschal Megan N, Miller Jenna, Jain Sankalp, Zakharov Alexey V, Rai Ganesha, Simeonov Anton, Baljinnyam Bolormaa, Zhuang Zhihao