by: Akinwumi Ishola Abeeb, Track «Chemoinformatics for Biophysical and Computational Chemistry», Ljubljana-Strasbourg, 2022
In this interview, we will talk about ADMET properties prediction, its challenges and some recommendations for students who desire to work in this field.
1) Please introduce yourself and tell a little bit about your scientific career.
Dr Medina-Franco, I hold a BSc in Chemistry, an MSc and a PhD from the National Autonomous University of Mexico (UNAM). I joined the University of Arizona as a postdoctoral fellow under Prof. Gerald Maggiora. In 2014 I joined UNAM and I am now Full-Time Research Professor. I lead the DIFACQUIM research group at UNAM. My research focuses on Chemoinformatics, molecular modelling and artificial intelligence with applications on epigenetic targets and natural products. I am a member of the National Researcher System, National Council of Science and Technology in Mexico, with the highest level, III. In 2017 I have been named a Fellow of the Royal Society of Chemistry (UK). I have been Visiting Professor at the University of Montreal in Quebec in 2019 and The University of Pereira, Colombia, in 2021. I have published 257 peer-reviewed papers and 24 book chapters and issued one international patent. I serve as Chief Editor of the section “In Silico Modeling and Artificial Intelligence” of Frontiers in Drug Discovery.
2) Please explain in a few words what "ADMET" is in drug discovery.
ADMET stands for Absorption, Distribution, Metabolism, Excretion and Toxicity. These are key pharmacokinetics properties of drugs that should be fulfilled.
3) Can you briefly discuss your experience using Chemoinformatics techniques in predicting ADMET properties?
My research group used well-established ADMET predicting tools to profile screening libraries, emphasizing natural product collections, see [1]. An example of an ADMET predicting tool is admetSAR (http://lmmd.ecust.edu.cn/admetsar2/).
4) Can you briefly describe some challenges of ADMET properties prediction?
ADMET profiling of compounds is undoubtedly valuable but should be used cautiously. As with many in silico predicting tools, positive and negative reference compounds should be used as described in [2]. The main challenge is that the prediction should not be considered as that; the estimations should be validated experimentally. ADMET properties are highly complex phenomena and hard to predict accurately.
5) What recommendations do you have for students who desire to work in this field?
Avoid the hype of AI and be careful before using "easy-to-use" tools (such as many online web servers) to predict ADMET properties. Study first the phenomena you want to study in silico, and "do not compute what you don’t understand".
References:
[1] Noemi Angeles Durán-Iturbide, Bárbara I. Díaz-Eufracio, and José L. Medina-Franco. In Silico ADME/Tox Profiling of Natural Products: A Focus on BIOFACQUIM. ACS Omega, 5 (26), 16076-16084. (2020). https://pubs.acs.org/doi/10.1021/acsomega.0c01581.
[2] Medina-Franco JL, Martinez-Mayorga K, Fernández-de Gortari E et al. Rationality over fashion and hype in drug design [version 1; peer review: 2 approved]. F1000Research, 10, (Chem Inf Sci):397. (2021). https://doi.org/10.12688/f1000research.52676.1.