Medicinal chemistry for 2020

A dual diffusion model enables 3D molecule generation and lead optimization based on target pockets

Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

MedChemLens: An Interactive Visual Tool to Support Direction Selection in Interdisciplinary Experimental Research of Medicinal Chemistry

Interdisciplinary experimental science (e.g., medicinal chemistry) refers to the disciplines that integrate knowledge from different scientific backgrounds and involve experiments in the research process. Deciding "in what direction to proceed" is critical for the success of the research in such disciplines, since the time, money, and resource costs of the subsequent research steps depend largely on this decision. However, such a direction identification task is challenging in that researchers need to integrate information from large-scale, heterogeneous materials from all associated disciplines and summarize the related publications of which the core contributions are often showcased in diverse formats. The task also requires researchers to estimate the feasibility and potential in future experiments in the selected directions. In this work, we selected medicinal chemistry as a case and presented an interactive visual tool, MedChemLens, to assist medicinal chemists in choosing their intended directions of research. This task is also known as drug target (i.e., disease-linked proteins) selection. Given a candidate target name, MedChemLens automatically extracts the molecular features of drug compounds from chemical papers and clinical trial records, organizes them based on the drug structures, and interactively visualizes factors concerning subsequent experiments. We evaluated MedChemLens through a within-subjects study (N=16). Compared with the control condition (i.e., unrestricted online search without using our tool), participants who only used MedChemLens reported faster search, better-informed selections, higher confidence in their selections, and lower cognitive load.

Development of New Potential Inhibitors of β1 Integrins through In Silico Methods-Screening and Computational Validation

Integrins are transmembrane receptors that play a critical role in many biological processes which can be therapeutically modulated using integrin blockers, such as peptidomimetic ligands. This work aimed to develop new potential β1 integrin antagonists using modeled receptors based on the aligned crystallographic structures and docked with three lead compounds (BIO1211, BIO5192, and TCS2314), widely known as α4β1 antagonists. Lead-compound complex optimization was performed by keeping intact the carboxylate moiety of the ligand, adding substituents in two other regions of the molecule to increase the affinity with the target. Additionally, pharmacokinetic predictions were performed for the ten best ligands generated, with the lowest docking interaction energy obtained for α4β1 and BIO5192. Results revealed an essential salt bridge between the BIO5192 carboxylate group and the Mg²⁺ MIDAS ion of the integrin. We then generated more than 200 new BIO5192 derivatives, some with a greater predicted affinity to α4β1. Furthermore, the significance of retaining the pyrrolidine core of the ligand and increasing the therapeutic potential of the new compounds is emphasized. Finally, one novel molecule (1592) was identified as a potential drug candidate, with appropriate pharmacokinetic profiles, similar dynamic behavior at the integrin interaction site compared with BIO5192, and a higher predicted affinity to VLA-4.

Rhinacanthin-C but Not -D Extracted from Rhinacanthus nasutus (L.) Kurz Offers Neuroprotection via ERK, CHOP, and LC3B Pathways

Neurodegenerative diseases present an increasing problem as the world's population ages; thus, the discovery of new drugs that prevent diseases such as Alzheimer's, and Parkinson's diseases are vital. In this study, Rhinacanthin-C and -D were isolated from Rhinacanthus nasustus, using ethyl acetate, followed by chromatography to isolate Rhinacanthin-C and -D. Both compounds were confirmed using NMR and ultra-performance-LCMS. Using glutamate toxicity in HT-22 cells, we measured cell viability and apoptosis, ROS build-up, and investigated signaling pathways. We show that Rhinacanthin-C and 2-hydroxy-1,4-naphthoquinone have neuroprotective effects against glutamate-induced apoptosis in HT-22 cells. Furthermore, we see that Rhinacanthin-C resulted in autophagy inhibition and increased ER stress. In contrast, low concentrations of Rhinacanthin-C and 2-hydroxy-1,4-naphthoquinone prevented ER stress and CHOP expression. All concentrations of Rhinacanthin-C prevented ROS production and ERK1/2 phosphorylation. We conclude that, while autophagy is present in HT-22 cells subjected to glutamate toxicity, its inhibition is not necessary for cryoprotection.

AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge

Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem. In this study, we developed a data-driven CASP application integrated with various portions of retrosynthesis knowledge called "ReTReK" that introduces the knowledge as adjustable parameters into the evaluation of promising search directions. The experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, indicating that the synthetic routes searched with the knowledge were preferred to those without the knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into a data-driven CASP application is expected to enhance the performance of both existing data-driven CASP applications and those under development.

“MedChemVR”: A Virtual Reality Game to Enhance Medicinal Chemistry Education

Medicinal chemistry (MC) is an indispensable component of the pharmacy curriculum. The pharmacists’ unique knowledge of a medicine’s chemistry enhances their understanding of the pharmacological activity, manufacturing, storage, use, supply, and handling of drugs. However, chemistry is a challenging subject for both teaching and learning. These challenges are typically caused by the inability of students to construct a mental image of the three-dimensional (3D) structure of a drug molecule from its two-dimensional presentations. This study explores a prototype virtual reality (VR) gamification option, as an educational tool developed to aid the learning process and to improve the delivery of the MC subject to students. The developed system is evaluated by a cohort of 41 students. The analysis of the results was encouraging and provided invaluable feedback for the future development of the proposed system.

Into the Fray! A Beginner's Guide to Medicinal Chemistry

Modern medicinal chemistry is a complex, multidimensional discipline that operates at the interface of the chemical and biological sciences. The medicinal chemistry contribution to drug discovery is typically described in the context of the well-recited linear progression of the drug discovery pipeline. However, compound optimization is idiosyncratic to each project, and clear definitions of hit and lead molecules and the subsequent progress along the pipeline becomes easily blurred. In addition, this description lacks insight into the entangled relationship between chemical and pharmacological properties, and thus provides limited guidance on how innovative medicinal chemistry strategies can be applied to solve optimization problems, regardless of the stage in the pipeline. Through discussion and illustrative examples, this article seeks to provide insights into the finesse of medicinal chemistry and the subtlety of balancing chemical properties pharmacology. In so doing, it aims to serve as an accessible and simple-to-digest guide for anyone who wishes to learn about the underlying principles of medicinal chemistry, in a context that has been decoupled from the pipeline description.

CompRet: a comprehensive recommendation framework for chemical synthesis planning with algorithmic enumeration

In computer-assisted synthesis planning (CASP) programs, providing as many chemical synthetic routes as possible is essential for considering optimal and alternative routes in a chemical reaction network. As the majority of CASP programs have been designed to provide one or a few optimal routes, it is likely that the desired one will not be included. To avoid this, an exact algorithm that lists possible synthetic routes within the chemical reaction network is required, alongside a recommendation of synthetic routes that meet specified criteria based on the chemist’s objectives. Herein, we propose a chemical-reaction-network-based synthetic route recommendation framework called “CompRet” with a mathematically guaranteed enumeration algorithm. In a preliminary experiment, CompRet was shown to successfully provide alternative routes for a known antihistaminic drug, cetirizine. CompRet is expected to promote desirable enumeration-based chemical synthesis searches and aid the development of an interactive CASP framework for chemists.

Recent Highlights in Anti-infective Medicinal Chemistry from South Africa

Global advancements in biological technologies have vastly increased the variety of and accessibility to bioassay platforms, while simultaneously improving our understanding of druggable chemical space. In the South African context, this has resulted in a rapid expansion in the number of medicinal chemistry programmes currently operating, particularly on university campuses. Furthermore, the modern medicinal chemist has the advantage of being able to incorporate data from numerous related disciplines into the medicinal chemistry process, allowing for informed molecular design to play a far greater role than previously possible. Accordingly, this review focusses on recent highlights in drug-discovery programmes, in which South African medicinal chemistry groups have played a substantive role in the design and optimisation of biologically active compounds which contribute to the search for promising agents for infectious disease.

Automated De Novo Drug Design: Are We Nearly There Yet?

Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.

Automating drug discovery

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

De Novo Design at the Edge of Chaos

Computational medicinal chemistry offers viable strategies for finding, characterizing, and optimizing innovative pharmacologically active compounds. Technological advances in both computer hardware and software as well as biological chemistry have enabled a renaissance of computer-assisted "de novo" design of molecules with desired pharmacological properties. Here, we present our current perspective on the concept of automated molecule generation by highlighting chemocentric methods that may capture druglike chemical space, consider ligand promiscuity for hit and lead finding, and provide fresh ideas for the rational design of customized screening of compound libraries.

Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors

Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.

Development of broad-spectrum antibiofilm drugs: strategies and challenges

ABSTRACT 

The severity of many chronic bacterial infections is mainly due to the biofilm mode of life adapted by pathogenic bacteria. The bacteria in biofilm-stage exhibit high resistance to host immune responses and antimicrobials, which complicates the treatment process and results in life threatening conditions. Most of the chronic infections are polymicrobial in nature. In order to combat the polymicrobial biofilm infections and to increase the efficiency of antimicrobials, there is an urgent need for broad-spectrum antibiofilm drugs. This review discusses the clinical needs and current status of broad-spectrum antibiofilm drugs with special emphasis on prospective strategies and hurdles in the process of new drug discovery.

A molecular-modeling toolbox aimed at bridging the gap between medicinal chemistry and computational sciences

In the current era of high-throughput drug discovery and development, molecular modeling has become an indispensable tool for identifying, optimizing and prioritizing small-molecule drug candidates. The required background in computational chemistry and the knowledge of how to handle the complex underlying protocols, however, might keep medicinal chemists from routinely using in silico technologies. Our objective is to encourage those researchers to exploit existing modeling technologies more frequently through easy-to-use graphical user interfaces. In this account, we present two innovative tools (which we are prepared to share with academic institutions) facilitating computational tasks commonly utilized in drug discovery and development: (1) the VirtualDesignLab estimates the binding affinity of small molecules by simulating and quantifying their binding to the three-dimensional structure of a target protein; and (2) the MD Client launches molecular dynamics simulations aimed at exploring the time-dependent stability of ligand–protein complexes and provides residue-based interaction energies. This allows medicinal chemists to identify sites of potential improvement in their candidate molecule. As a case study, we present the application of our tools towards the design of novel antagonists for the FimH adhesin.