Aller au contenu principal
Profil bibliographique

Jackson Chief Elk

Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.

18Publications signalées
517Citations signalées
1Affiliations récentes

Les institutions déclarées

Les domaines associés

Computational Drug Discovery MethodsMachine Learning in Materials ScienceFree Radicals and AntioxidantsReceptor Mechanisms and SignalingProtein Structure and Dynamics

Les publications récentes

2025 article OpenAlex

Predicting Resistance to Small Molecule Kinase Inhibitors

Anu Nagarajan, Katherine Amberg-Johnson, Evan Paull, Kunling Huang et autres

Drug resistance is a critical challenge in treating diseases like cancer and infectious disease. This study presents a novel computational workflow for predicting on-target resistance mutations to small molecule inhibitors (SMIs). The approach integrates genetic models with alchemical free energy perturbation (FEP+) …

us (code pays fourni par la source)

7 citations Journal of Chemical Information and Modeling
Accès ouvert 2024 preprint OpenAlex

Predicting resistance to small molecule kinase inhibitors

Anu Nagarajan, Katherine Amberg-Johnson, Evan Paull, Kunling Huang et autres

Drug resistance is a critical challenge in treating diseases like cancer and infectious disease. This study presents a novel computational workflow for predicting on-target resistance mutations to small molecule inhibitors (SMIs). The approach integrates genetic models with alchemical free energy perturbation (FEP+) …

us (code pays fourni par la source)

0 citations ChemRxiv
Accès ouvert 2024 article OpenAlex

Advancing material property prediction: using physics-informed machine learning models for viscosity

Alex K. Chew, Matthew Sender, Zachary Kaplan, Anand R. Chandrasekaran et autres

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To …

us (code pays fourni par la source)

78 citations Journal of Cheminformatics
Accès ouvert 2024 preprint OpenAlex

Advancing Material Property Prediction: Using Physics-Informed Machine Learning Models for Viscosity

Alex K. Chew, Matthew Sender, Zachary Kaplan, Anand R. Chandrasekaran et autres

In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To …

us (code pays fourni par la source)

1 citation ChemRxiv
2023 article OpenAlex

A Computational Physics-based Approach to Predict Unbound Brain-to-Plasma Partition Coefficient, K p,uu

Morgan Lawrenz, Mats Svensson, Mitsunori Kato, Karen H. Dingley et autres

The blood–brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small-molecule central nervous system (CNS) drugs is characterized by a high unbound brain/plasma ratio (K p,uu ). While various …

us (code pays fourni par la source)

23 citations Journal of Chemical Information and Modeling
2023 article OpenAlex

Epik: p K a and Protonation State Prediction through Machine Learning

Ryne C. Johnston, Kun Yao, Zachary Kaplan, Monica Chelliah et autres

Epik version 7 is a software program that uses machine learning for predicting the p K a values and protonation state distribution of complex, druglike molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 p K …

us (code pays fourni par la source)

361 citations Journal of Chemical Theory and Computation
Accès ouvert 2023 preprint OpenAlex

Epik: pKa and Protonation State Prediction through Machine Learning

Ryne C. Johnston, Kun Yao, Zachary Kaplan, Monica Chelliah et autres

Epik version 7 is a software program that uses machine learning for predicting the pKa values and protonation state distribution of complex, drug-like molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 pKa values across broad …

us (code pays fourni par la source)

8 citations ChemRxiv
Accès ouvert 2023 preprint OpenAlex

Epik: pKa and Protonation State Prediction through Machine Learning

Ryne C. Johnston, Kun Yao, Zachary Kaplan, Monica Chelliah et autres

Epik version 7 is a software program that uses machine learning for predicting the pKa values and protonation state distribution of complex, drug-like molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 pKa values across broad …

us (code pays fourni par la source)

3 citations ChemRxiv
Accès ouvert 2023 preprint OpenAlex

A Computational Physics-based Approach to Predict Unbound Brain-to-Plasma Partition Coefficient, Kp,uu

Morgan Lawrenz, Mats Svensson, Mitsunori Kato, Karen H. Dingley et autres

The blood-brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small molecule CNS drugs is characterized by a high unbound brain/plasma ratio (Kp,uu). While various medicinal chemistry strategies and …

us (code pays fourni par la source)

2 citations ChemRxiv
Accès ouvert 2023 preprint OpenAlex

Epik: pKa and Protonation State Prediction through Machine Learning

Ryne C. Johnston, Kun Yao, Zachary Kaplan, Monica Chelliah et autres

Epik version 7 is a software program that uses machine learning for predicting the pKa values and protonation state distribution of complex, drug-like molecules. Using an ensemble of atomic graph convolutional neural networks (GCNNs) trained on over 42,000 pKa values across broad …

us (code pays fourni par la source)

10 citations ChemRxiv
2022 conference-abstract OpenAlex

Abstract 2570: Discovery of potent, selective, and orally available WEE1 inhibitors that demonstrate increased DNA damage and mitosis in tumor cells leading to tumor regression in vivo

Shaoxian Sun, Sarah Silvergleid, Aleksey I. Gerasyuto, Jiashi Wang et autres

Abstract WEE1 inhibits the activation of both CDK1 and CDK2 through phosphorylation of Tyr15, allowing DNA damage repair before entering mitosis, thereby regulating the cell cycle in S and G2/M phases. Inhibition of WEE1 could result in premature progression through the G2/M …

us (code pays fourni par la source)

0 citations Cancer Research
2021 conference-abstract OpenAlex

Abstract 1277: Discovery of novel CDC7 inhibitors that disrupt cell cycle dynamics and show anti-proliferative effects in cancer cells

Lyuben M. Tsvetkov, Adam Levinson, Xianhai Huang, Sayan Mondal et autres

Abstract Introduction: CDC7 is a serine/threonine protein kinase that phosphorylates the MCM2-7 helicase complex, a required step in DNA replication initiation. CDC7 has emerged as an attractive target for cancer treatment because of high expression in a number of tumors (e.g. ovarian, …

us (code pays fourni par la source)

1 citation Cancer Research

BNTIC News n’est pas le producteur de ces données. Les publications sont interrogées à la demande dans Crossref, OpenAIRE, DOAJ, Europe PMC, HAL, DataCite, AfricArXiv, ROR et la Banque mondiale, sans clé d’accès. OpenAlex reste optionnel. Aucun service payant n’est nécessaire et aucune donnée externe n’est enregistrée en base. Consulter les sources et leurs limites.