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The Role of Natural Products in COVID-19
Published in Hanadi Talal Ahmedah, Muhammad Riaz, Sagheer Ahmed, Marius Alexandru Moga, The Covid-19 Pandemic, 2023
Iqra Akhtar, Sumera Javad, Tehreema Iftikhar, Amina Tariq, Hammad Majeed, Asma Ahmad, Muhammad Arfan, M. Zia-Ul-Haq
Natural products are known to possess a broader diversity in chemical space and, as a result, have produced a profound impact on chemical biology and drug development (Table 11.1). Drug discovery and formulation using plant metabolites as raw material has always been a challenging task. This process of drug discovery usually consists of following steps: Extraction of bioactiveIsolation of theseCharacterization;Structure elucidation;Pharmacological investigation;Preclinical, and clinical trials;Final approval.
Ayurveda Renaissance – Quo Vadis?
Published in D. Suresh Kumar, Ayurveda in the New Millennium, 2020
Bioactive compounds isolated from herbs are favorable as lead structures for drug discovery. Though it is generally accepted that they show great structural diversity and could play a protagonist role for discovering new drugs, evaluating this diverse chemical space efficiently has remained a challenge for medicinal chemists and pharmacologists. Isolation of all the available bioactive compounds, followed by random screening is next to impossible (Polur et al. 2011). Therefore, experts in chemoinformatics have adopted the application of computational approaches for the identification of bioactive molecules (Harvey 2008). Systems biology approaches have been used to explore the molecular basis of Chinese medicine and to relate its terminology in the context of Western medicine (Yi et al. 2010).
On Biocatalysis as Resourceful Methodology for Complex Syntheses: Selective Catalysis, Cascades and Biosynthesis
Published in Peter Grunwald, Pharmaceutical Biocatalysis, 2019
Andreas Sebastian Klein, Thomas Classen, Jörg Pietruszka
Natural compounds have been used as pharmaceuticals since ancient times. While fragrances or dyes were substituted by chemical compounds in virtually all cases, natural compounds still are important as active agents. Feher and Schmidt (2003) analyzed a plethora of compounds with respect to their physico-chemical properties such as molecular weight, number of stereogenic centers etc. for thousands of combinatorial chemicals, natural compounds, and pharmaceuticals. The principal component analysis revealed that the chemical space of pharmaceuticals and natural compounds are more congruent than the chemical space of combinatorial compounds. However, natural compounds are rather complex in terms of numbers of functional moieties as well as stereogenic elements (see Fig. 21.1), which renders their synthesis often as particular challenge. Thus, it is important to have selective synthetic tools for these jobs. Besides human-made synthetic tools, biocatalytic methods acquired importance during the last decades (Bornscheuer, 2018; Classen and Pietruszka, 2017; Gröger, 2018; Patel, 2018; Sun et al., 2018).
In silico QSAR modeling to predict the safe use of antibiotics during pregnancy
Published in Drug and Chemical Toxicology, 2023
Feyza Kelleci Çelik, Gül Karaduman
The quality of the collected data is the most critical factor in machine learning model building (Hongbin et al.2018). As a result, before employing machine learning methods, we cleaned and verified the quality of the data. First, we identified and removed corrupted and irrelevant information data from the dataset. Then, the inorganic compounds, salts, and aromaticity in the molecule were removed, followed by the removal of duplicated compounds. After the data cleaning process, 97 antibiotics remained as QSAR-ready structures. Then, the remaining 97 antibiotics were divided into the training and external validation sets by using an unsupervised filter in the machine learning software Waikato Environment for Knowledge Analysis (WEKA) version 3.9.5 (Frank et al.2016). After splitting the dataset in 8:2 ratio, the training set contained 80 compounds, and the external validation set contained 17 compounds (Table 1). We verified that the training set compounds span the entire chemical space for all of the dataset compounds after dividing the dataset. This is an important concept for the meaningfulness of the QSAR models.
Today’s drug discovery and the shadow of the rule of 5
Published in Expert Opinion on Drug Discovery, 2023
Contemporary drug discovery clearly is not a one-size-fits-all discipline – practitioners should model, predict, measure, and learn from their observations and understand that different physical make-ups (the very definition of ‘chemical space’) follow different principles. Yet, there remains much to learn, as Lipinski, Lombardo, Dominy, and Feeny so ably did so many years ago: as molecules and medicines evolve, so do methods and descriptors to characterize them. Compromise in drug discovery frequently is driven by the need for permeation, which is clearly related to lipophilicity, yet ever more evidence around the caveats of ‘compound classes that are substrates for biological transporters’ suggests further means of flouting any rules, but within the bounds of what nature recognizes. Drugs are designed to modulate targets, exploit natural processes, and circumvent defense mechanisms that resulted from billions of years of evolution; the era of high-throughput methods, metrics, and addiction to potency shifted focus away from nature’s principles that clearly prevailed in the pre-HTS era.
Reinforcement learning for systems pharmacology-oriented and personalized drug design
Published in Expert Opinion on Drug Discovery, 2022
Ryan K. Tan, Yang Liu, Lei Xie
Figure 3 illustrates an RL framework for systems pharmacology-oriented personalized lead optimization and drug design. The molecule and the in vivo state together constitute an agent state. A molecule generator (agent) will first take an action based on the current state and policy to generate a new molecule or modify a seed molecule (e.g., replacing a hydrogen atom with a methyl group). Then, multiplex phenotypic responses (cell viability, drug-target profile, chemical-induced gene expression, pharmacokinetics, etc.) in an individual patient (environment) will be predicted for the newly generated molecule by machine learning, biophysics, systems biology, or other methods, and these responses are used as the reward for policy training. A new policy will be learned based on observed actions, states, and rewards by performing an optimization with a multi-objective RL (MORL) algorithm, and a new molecule will be generated again from the updated policy. Unlike target-based compound screening where only chemical space is needed to be explored, systems pharmacology-oriented personalized drug discovery needs to optimize the interplay of chemicals, the druggable genome, and high-dimensional omics characterizations of disease models or patients. Several barriers need to be overcome when applying RL to systems pharmacology and precision medicine. These include the exploration of out-of-distribution samples, generalization power of RL, adaptive multi-objective optimization [87–89], and activity cliffs of quantitative structure–activity relationship (QSAR) space [90].