[HTML][HTML] Machine learning for metabolomics research in drug discovery

DD Martinelli - Intelligence-Based Medicine, 2023 - Elsevier
In a pharmaceutical context, metabolomics is an underexplored area of research.
Nevertheless, its utility in clinical pathology, biomarker discovery, metabolic subty**, and …

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

T Lane, DP Russo, KM Zorn, AM Clark… - Molecular …, 2018 - ACS Publications
Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences
and 1.7 million deaths. The need to develop new treatments for those infected with …

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

S Ekins, J Lage de Siqueira-Neto… - PLoS neglected …, 2015 - journals.plos.org
Background Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic
parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is …

Machine Learning Models for Mycobacterium tuberculosis  In Vitro Activity: Prediction and Target Visualization

TR Lane, F Urbina, L Rank, J Gerlach… - Molecular …, 2021 - ACS Publications
Tuberculosis (TB) is a major global health challenge, with approximately 1.4 million deaths
per year. There is still a need to develop novel treatments for patients infected with …

Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery

S Ekins, RC Reynolds, H Kim, MS Koo, M Ekonomidis… - Chemistry & biology, 2013 - cell.com
Identification of unique leads represents a significant challenge in drug discovery. This
hurdle is magnified in neglected diseases such as tuberculosis. We have leveraged public …

Combining Computational Methods for Hit to Lead Optimization in Mycobacterium Tuberculosis Drug Discovery

S Ekins, JS Freundlich, JV Hobrath… - Pharmaceutical …, 2014 - Springer
Purpose Tuberculosis treatments need to be shorter and overcome drug resistance. Our
previous large scale phenotypic high-throughput screening against Mycobacterium …

Enhancing Hit Identification in Mycobacterium tuberculosis Drug Discovery Using Validated Dual-Event Bayesian Models

S Ekins, RC Reynolds, SG Franzblau, B Wan… - PloS one, 2013 - journals.plos.org
High-throughput screening (HTS) in whole cells is widely pursued to find compounds active
against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis …

Predictive modeling targets thymidylate synthase ThyX in Mycobacterium tuberculosis

K Djaout, V Singh, Y Boum, V Katawera, HF Becker… - Scientific reports, 2016 - nature.com
There is an urgent need to identify new treatments for tuberculosis (TB), a major infectious
disease caused by Mycobacterium tuberculosis (Mtb), which results in 1.5 million deaths …

Evolution of a thienopyrimidine antitubercular relying on medicinal chemistry and metabolomics insights

SG Li, C Vilchèze, S Chakraborty, X Wang, H Kim… - Tetrahedron letters, 2015 - Elsevier
The metabolic instability of an antitubercular small molecule CD117 was addressed through
iterative alteration of a key sulfide substituent and interrogation of the effect on growth …

Looking Back to the Future: Predicting in Vivo Efficacy of Small Molecules versus Mycobacterium tuberculosis

S Ekins, R Pottorf, RC Reynolds… - Journal of chemical …, 2014 - ACS Publications
Selecting and translating in vitro leads for a disease into molecules with in vivo activity in an
animal model of the disease is a challenge that takes considerable time and money. As an …