Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Radical C−H bond functionalization provides a versatile approach for elaborating heterocyclic compounds. The synthetic design of this transformation relies heavily on the knowledge of regioselectivity, while a quantified and efficient regioselectivity prediction approach is still elusive. Herein, we report the feasibility of using a machine learning model to predict the transition state barrier from the computed properties of isolated reactants. This enables rapid and reliable regioselectivity prediction for radical C−H bond functionalization of heterocycles. The Random Forest model with physical organic features achieved 94.2 % site accuracy and 89.9 % selectivity accuracy in the out-of-sample test set. The prediction performance was further validated by comparing the machine learning results with additional substituents, heteroarene scaffolds and experimental observations. This work revealed that the combination of mechanism-based computational statistics and machine learning model can serve as a useful strategy for selectivity prediction of organic transformations.     

Taba: A Tool to Analyze the Binding Affinity

Evaluation of ligand-binding affinity using the atomic coordinates of a protein-ligand complex is a challenge from the computational point of view. The availability of crystallographic structures of complexes with binding affinity data opens the possibility to create machine-learning models targeted to a specific protein system. Here, we describe a new methodology that combines a mass-spring system approach with supervised machine-learning techniques to predict the binding affinity of protein-ligand complexes. The combination of these techniques allows exploring the scoring function space, generating a model targeted to a protein system of interest. The new model shows superior predictive performance when compared with classical scoring functions implemented in the programs Molegro Virtual Docker, AutoDock4, and AutoDock Vina. We implemented this methodology in a new program named Taba. Taba is implemented in Python and available to download under the GNU license at https://github.com/azevedolab/taba . © 2019 Wiley Periodicals, Inc.     

Chemical promenades: Exploring potential-energy surfaces with immersive virtual reality

The virtual-reality framework AVATAR (Advanced Virtual Approach to Topological Analysis of Reactivity) for the immersive exploration of potential-energy landscapes is presented. AVATAR is based on modern consumer-grade virtual-reality technology and builds on two key concepts: (a) the reduction of the dimensionality of the potential-energy surface to two process-tailored, physically meaningful generalized coordinates, and (b) the analogy between the evolution of a chemical process and a pathway through valleys (potential wells) and mountain passes (saddle points) of the associated potential energy landscape. Examples including the discovery of competitive reaction paths in simple A + BC collisional systems and the interconversion between conformers in ring-puckering motions of flexible rings highlight the innovation potential that augmented and virtual reality convey for teaching, training, and supporting research in chemistry.     

Combining electronic properties and virtual screening for the development of new antioxidants: Trolox-like compounds as application example

Oxidative stress is an imbalance between the production of free radicals and the antioxidant defenses of the organism. Heart diseases, anemia, inflammation, and neurodegenerative disorders have been associated with this biological condition. Trolox is a notable antioxidant drug similar to vitamin E, and it is used to decrease the oxidative stress or repair the damage caused by it. In this work, the virtual screening technique is applied to identify compounds with antioxidant activities similar to Trolox. The antioxidant activity of these compounds was assessments by the mechanisms of hydrogen atom transfer and single electron transfer. Properties such as bond dissociation enthalpy, adiabatic ionization potential, Gibbs free reaction energy, spin density, highest occupied molecular orbital (HOMO), and GAP (HOMO-LUMO) energies, obtained from the DFT approach, point out to the predominance of the HAT mechanism for the antioxidant action of these compounds. The obtained results contribute to a better understanding of the chemical and physical properties responsible for antioxidant activity and the design of new antioxidant agents.     

Ensemble Docking Coupled to Linear Interaction Energy Calculations for Identification of Coronavirus Main Protease (3CLpro) Non-Covalent Small-Molecule Inhibitors

SARS-CoV-2, or severe acute respiratory syndrome coronavirus 2, represents a new strain of Coronaviridae. In the closing 2019 to early 2020 months, the virus caused a global pandemic of COVID-19 disease. We performed a virtual screening study in order to identify potential inhibitors of the SARS-CoV-2 main viral protease (3CL^pro or M^pro). For this purpose, we developed a novel approach using ensemble docking high-throughput virtual screening directly coupled with subsequent Linear Interaction Energy (LIE) calculations to maximize the conformational space sampling and to assess the binding affinity of identified inhibitors. A large database of small commercial compounds was prepared, and top-scoring hits were identified with two compounds singled out, namely 1-[(R)-2-(1,3-benzimidazol-2-yl)-1-pyrrolidinyl]-2-(4-methyl-1,4-diazepan-1-yl)-1-ethanone and [({(S)-1-[(1H-indol-2-yl)methyl]-3-pyrrolidinyl}methyl)amino](5-methyl-2H-pyrazol-3-yl)formaldehyde. Moreover, we obtained a favorable binding free energy of the identified compounds, and using contact analysis we confirmed their stable binding modes in the 3CL^pro active site. These compounds will facilitate further 3CL^pro inhibitor design.     

Searching Hit Potential Antimicrobials in Natural Compounds Space against Biofilm Formation

Biofilms are communities of microorganisms that can colonize biotic and abiotic surfaces and thus play a significant role in the persistence of bacterial infection and resistance to antimicrobial. About 65% and 80% of microbial and chronic infections are associated with biofilm formation, respectively. The increase in infections by multi-resistant bacteria instigates the need for the discovery of novel natural-based drugs that act as inhibitory molecules. The inhibition of diguanylate cyclases (DGCs), the enzyme implicated in the synthesis of the second messenger, cyclic diguanylate (c-di-GMP), involved in the biofilm formation, represents a potential approach for preventing the biofilm development. It has been extensively studied using PleD protein as a model of DGC for in silico studies as virtual screening and as a model for in vitro studies in biofilms formation. This study aimed to search for natural products capable of inhibiting the Caulobacter crescentus enzyme PleD. For this purpose, 224,205 molecules from the natural products ZINC15 database, have been evaluated through molecular docking and molecular dynamic simulation. Our results suggest trans-Aconitic acid (TAA) as a possible starting point for hit-to-lead methodologies to obtain new inhibitors of the PleD protein and hence blocking the biofilm formation.     

Natural Products Extracted from Fungal Species as New Potential Anti-Cancer Drugs: A Structure-Based Drug Repurposing Approach Targeting HDAC7

Mushrooms can be considered a valuable source of natural bioactive compounds with potential polypharmacological effects due to their proven antimicrobial, antiviral, antitumor, and antioxidant activities. In order to identify new potential anticancer compounds, an in-house chemical database of molecules extracted from both edible and non-edible fungal species was employed in a virtual screening against the isoform 7 of the Histone deacetylase (HDAC). This target is known to be implicated in different cancer processes, and in particular in both breast and ovarian tumors. In this work, we proposed the ibotenic acid as lead compound for the development of novel HDAC7 inhibitors, due to its antiproliferative activity in human breast cancer cells (MCF-7). These promising results represent the starting point for the discovery and the optimization of new HDAC7 inhibitors and highlight the interesting opportunity to apply the “drug repositioning” paradigm also to natural compounds deriving from mushrooms.     

Emission Control by Molecular Manipulation of Double‐Paddled Binuclear PtII Complexes at the Air‐Water Interface

Molecular functions depend on conformations and motions of the corresponding molecular species. An air–water interface is a suitable asymmetric field for the control of molecular conformations and motions under a small applied force. In this work, double‐paddled binuclear Pt^II complexes containing pyrazole rings linked by alkyl spacers were synthesized and their orientations and emission properties dynamically manipulated at the air–water interface. The complexes emerge from water with concurrent variation of interface orientation of the planes of the Pt^II complexes from perpendicular to parallel during mechanical compression suggesting a unique ‘submarine emission‘. Phosphorescence of the complexes is quenched at the air–water interface prior to monolayer formation with intensities subsequently rapidly increasing during monolayer compression. These results indicate that asymmetric reactions and motions might be controlled by applying mechanical force at the air–water interface.     

Drug Design—Past,Present, Future

Drug design is a complex pharmaceutical science with a long history. Many achievements have been made in the field of drug design since the end of 19th century, when Emil Fisher suggested that the drug–receptor interaction resembles the key and lock interplay. Gradually, drug design has been transformed into a coherent and well-organized science with a solid theoretical background and practical applications. Now, drug design is the most advanced approach for drug discovery. It utilizes the innovations in science and technology and includes them in its wide-ranging arsenal of methods and tools in order to achieve the main goal: discovery of effective, specific, non-toxic, safe and well-tolerated drugs. Drug design is one of the most intensively developing modern sciences and its progress is accelerated by the implication of artificial intelligence. The present review aims to capture some of the most important milestones in the development of drug design, to outline some of the most used current methods and to sketch the future perspective according to the author’s point of view. Without pretending to cover fully the wide range of drug design topics, the review introduces the reader to the content of Molecules’ Special Issue “Drug Design—Science and Practice”.