Computer-aided drug design platform using PyMOL

The understanding and optimization of protein-ligand interactions are instrumental to medicinal chemists investigating potential drug candidates. Over the past couple of decades, many powerful standalone tools for computer-aided drug discovery have been developed in academia providing insight into protein-ligand interactions. As programs are developed by various research groups, a consistent user-friendly graphical working environment combining computational techniques such as docking, scoring, molecular dynamics simulations, and free energy calculations is needed. Utilizing PyMOL we have developed such a graphical user interface incorporating individual academic packages designed for protein preparation (AMBER package and Reduce), molecular mechanics applications (AMBER package), and docking and scoring (AutoDock Vina and SLIDE). In addition to amassing several computational tools under one interface, the computational platform also provides a user-friendly combination of different programs. For example, utilizing a molecular dynamics (MD) simulation performed with AMBER as input for ensemble docking with AutoDock Vina. The overarching goal of this work was to provide a computational platform that facilitates medicinal chemists, many who are not experts in computational methodologies, to utilize several common computational techniques germane to drug discovery. Furthermore, our software is open source and is aimed to initiate collaborative efforts among computational researchers to combine other open source computational methods under a single, easily understandable graphical user interface.     

Computer-aided drug design: lead discovery and optimization

Over the past decade, there have been remarkable advances in the area of computer-aided drug design (CADD), which has been applied at almost all stages in the drug discovery pipeline. The generation of initial lead compounds and the subsequent optimization aimed at improving potency and pharmacological properties are the core activities among all. The development in these aspects over the past years will be the focus of this review.     

Computer-aided drug discovery research at a global contract research organization

Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.     

Computer-aided design of formulated products

Formulated products represent a particular class of complex chemical products, and their design is typically based on experience and extensive experimentation. Although still at an early stage, and despite that their potential is not fully accessed and not fully used by the industry, computer-aided design (CAD) methods and tools offer many possibilities in the design of formulated products. The CAD methodology based on computerized models enables the formulation chemists to speed up the design process, without completely replacing experiments.In this work, we summarize previous studies in the field and present important elements of the CAD framework, emphasizing estimation methods for key target properties, link to specifications, and finally, some case studies will illustrate how the CAD framework can be used in practice for formulated products.     

Computer-aided design of promising photochemical alkoxy radical precursors

A computer-aided design of alkoxyl radical precursors is performed. The new precursors should combine the advantages of N-alkoxypyridine-2(1H)thiones (less reactive radicals) and N-alkoxythiazole-2(3H)thiones (stable with respect to daylight). Additionally, the radical liberation process should be initiated by light with a wavelength of around 350 nm. To find promising compounds, 18 test candidates were obtained by a systematic variation of the parent compound N-alkoxythiazole-2(3H)thione. The properties of the test molecules were computed by a protocol that was already successfully used to rationalize the photochemical behavior of N-alkoxypyridine-2(1H)thiones and N-alkoxythiazole-2(3H)thiones. The computations identify two promising new compounds. For N-methoxy-(1,3)dihydro-[1,3]azaphosphole-2-thione (6a), they predict that the fragmentation process will be initiated by an absorption at 348 nm. An analysis of its fragmentation process indicates that the free excess energy of the resulting radicals should more resemble the situation found for N-alkoxypyridine-2(1H)thiones. For N-methoxy-(1,3)dihydro-pyrrole-2-thione (3a), the excitation energy is somewhat higher (330 nm), but the computed fragmentation paths again indicate that the remaining excess energy of the released radicals is quite favorable. The test molecules also contained the experimentally well-known N-methoxypyridine-2(1H)one (1b). For this molecule, our computed data rationalizes nicely the experimental findings.     

The evolution of drug design at Merck Research Laboratories

On October 5, 1981, Fortune magazine published a cover article entitled the “Next Industrial Revolution: Designing Drugs by Computer at Merck”. With a 40+ year investment, we have been in the drug design business longer than most. During its history, the Merck drug design group has had several names, but it has always been in the “design” business, with the ultimate goal to provide an actionable hypothesis that could be tested experimentally. Often the result was a small molecule but it could just as easily be a peptide, biologic, predictive model, reaction, process, etc. To this end, the concept of design is now front and center in all aspects of discovery, safety assessment and early clinical development. At present, the Merck design group includes computational chemistry, protein structure determination, and cheminformatics. By bringing these groups together under one umbrella, we were able to align activities and capabilities across multiple research sites and departments. This alignment from 2010 to 2016 resulted in an 80% expansion in the size of the department, reflecting the increase in impact due to a significant emphasis across the organization to “design first” along the entire drug discovery path from lead identification (LID) to first in human (FIH) dosing. One of the major advantages of this alignment has been the ability to access all of the data and create an adaptive approach to the overall LID to FIH pathway for any modality, significantly increasing the quality of candidates and their probability of success. In this perspective, we will discuss how we crafted a new strategy, defined the appropriate phenotype for group members, developed the right skillsets, and identified metrics for success in order to drive continuous improvement. We will not focus on the tactical implementation, only giving specific examples as appropriate.     

Computer-aided molecular modeling and design of DNA-inserting molecules

Summary Intercalators are molecules capable of sliding between base pairs without disturbing the overall stacking pattern. In addition, there may exist molecules capable of inserting into a base pair thereby disrupting the hydrogen bonds and replacing them with new hydrogen bonds. A molecule probably capable of inserting, i.e., an insertor, is the diketopiperazine cyclo-[Gly-Gly] (1). A barbiturate (2), alloxan (3), a pyrimidine derivative (4) and a hydantoin (5) were also studied as possible insertors. Furthermore, molecules such as ethyleneurea (6), succinimide (7), as well as a malonamide derivative (8) and oxamide derivatives (9–11) were studied in order to investigate the arrangement and the number of hydrogen bonds necessary for insertion. Molecules 12–14 were designed and studied for their capacity to act as bisinsertors and/or bisintercalators. These molecules feature two diketopiperazine moieties which are connected via a diphenyl(thio)ether, i.e., 12 and 13, or a bisphenol A spacer, i.e., 14. The latter molecule (14) seems a promising candidate as a bisinsertor.     

Computer-aided drug design of falcipain inhibitors: virtual screening, structure-activity relationships, hydration site thermodynamics, and reactivity analysis

Falcipains (FPs) are hemoglobinases of Plasmodium falciparum that are validated targets for the development of antimalarial chemotherapy. A combined ligand- and structure-based virtual screening of commercial databases was performed to identify structural analogs of virtual screening hits previously discovered in our laboratory. A total of 28 low micromolar inhibitors of FP-2 and FP-3 were identified and the structure-activity relationship (SAR) in each series was elaborated. The SAR of the compounds was unusually steep in some cases and could not be explained by a traditional analysis of the ligand-protein interactions (van der Waals, electrostatics, and hydrogen bonds). To gain further insights, a statistical thermodynamic analysis of explicit solvent in the ligand binding domains of FP-2 and FP-3 was carried out to understand the roles played by water molecules in binding of these inhibitors. Indeed, the energetics associated with the displacement of water molecules upon ligand binding explained some of the complex trends in the SAR. Furthermore, low potency of a subset of FP-2 inhibitors that could not be understood by the water energetics was explained in the context of poor chemical reactivity of the reactive centers of these compounds. The present study highlights the importance of considering energetic contributors to binding beyond traditional ligand-protein interactions.     

Computer-aided drug design: A free energy perturbation study on the binding of methyl-substituted pterins and N5-deazapterins to dihydrofolate reductase

Summary Molecular dynamics simulation and free energy perturbation techniques have been used to study the relative binding free energies of 8-methylpterins and 8-methyl-N5-deazapterins to dihydrofolate reductase (DHFR). Methyl-substitution at the 5, 6 and 7 positions in the N-heterocyclic ring gives rise to a variety of ring substituent patterns and biological activity: several of these methyl derivatives of the 8-methyl parent compounds (8-methylpterin and 8-methyl-N5-deazapterin) have been identified as substrates or inhibitors of vertebrate DHFR in previous work. The calculated free energy differences reveal that the methyl-substituted compounds are thermodynamically more stable than the primary compounds (8-methylpterin and 8-methyl-N5-deazapterin) when bound to the enzyme, due largely to hydrophobic hydration phenomena. Methyl substitution at the 5 and/or 7 positions in the 6-methyl-substituted compounds has only a small effect on the stability of ligand binding. Furthermore, repulsive interactions between the 6-methyl substituent and DHFR are minimal, suggesting that the 6-methyl position is optimal for binding. The results also show that similarly substituted 8-methylpterins and 8-methyl-N5-deazapterins have very similar affinities for binding to DHFR. The computer simulation predictions are in broad agreement with experimental data obtained from kinetic studies, i.e. 6,8-dimethylpterin is a more efficient substrate than 8-methylpterin and 6,8-dimethyl-N5-deazapterin is a better inhibitor than 8-methyl-N5-deazapterin.