Force Fields for Small Molecules

Lin, Fang-Yu; MacKerell, Alexander D.

doi:10.1007/978-1-4939-9608-7_2

Cited by 41 publications

(38 citation statements)

References 223 publications

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“…Of note, various force fields have been developed for different types of molecules. In the context of our review, force fields for proteins [5], biological lipids [6], and small molecules [7] are of particular relevance (see Table 1 for examples of current commonly used force fields). Force fields are also employed to compute energies in molecular mechanics (MM) applications [8].…”

Section: Classical Molecular Dynamics Simulationsmentioning

confidence: 99%

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

et al. 2020

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Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

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Section: Classical Molecular Dynamics Simulationsmentioning

confidence: 99%

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

et al. 2020

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“…Importantly, these popular class I [18,19] MM force fields have well-characterized drawbacks, in part, because they omit a number of important energetic contributions known to limit their ability to achieve chemical accuracy [7,20,21]. For example, while moving to more complex electrostatics models which include fixed multipoles and polarizable dipoles [22] are promising, the development of polarizable force fields that broadly deliver accuracy gains has proven challenging [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials

Rufa

Macdonald

Fass

et al. 2020

Preprint

106

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Alchemical free energy methods with molecular mechanics (MM) force fields are now widely used in the prioritization of small molecules for synthesis in structure-enabled drug discovery projects because of their ability to deliver 1–2 kcal mol−1 accuracy in well-behaved protein-ligand systems. Surpassing this accuracy limit would significantly reduce the number of compounds that must be synthesized to achieve desired potencies and selectivities in drug design campaigns. However, MM force fields pose a challenge to achieving higher accuracy due to their inability to capture the intricate atomic interactions of the physical systems they model. A major limitation is the accuracy with which ligand intramolecular energetics—especially torsions—can be modeled, as poor modeling of torsional profiles and coupling with other valence degrees of freedom can have a significant impact on binding free energies. Here, we demonstrate how a new generation of hybrid machine learning / molecular mechanics (ML/MM) potentials can deliver significant accuracy improvements in modeling protein-ligand binding affinities. Using a nonequilibrium perturbation approach, we can correct a standard, GPU-accelerated MM alchemical free energy calculation in a simple post-processing step to efficiently recover ML/MM free energies and deliver a significant accuracy improvement with small additional computational effort. To demonstrate the utility of ML/MM free energy calculations, we apply this approach to a benchmark system for predicting kinase:inhibitor binding affinities—a congeneric ligand series for non-receptor tyrosine kinase TYK2 (Tyk2)—wherein state-of-the-art MM free energy calculations (with OPLS2.1) achieve inaccuracies of 0.93±0.12 kcal mol−1 in predicting absolute binding free energies. Applying an ML/MM hybrid potential based on the ANI2x ML model and AMBER14SB/TIP3P with the OpenFF 1.0.0 (“Parsley”) small molecule force field as an MM model, we show that it is possible to significantly reduce the error in absolute binding free energies from 0.97 [95% CI: 0.68, 1.21] kcal mol−1 (MM) to 0.47 [95% CI: 0.31, 0.63] kcal mol−1 (ML/MM).

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“…The SPECS database (http://www.specs.net), which contains~350,000 compounds, was used for the screening. The 20,000 ligands were prepared using the "Prepare Ligand" module by the CHARMM force field 25 .…”

Section: Ligand Structuresmentioning

confidence: 99%

“…With the continuous advancement of computing technology, computer-aided drug design has become a popular method for preclinical drug research with its unique advantages of high efficiency and low cost 23 . Novel core structure LibDock Model 24 , CHARMM force field 25 , and CDOCKER molecular docking 26 were reported as an effective virtual screening strategy. In this study, we employed a combinatorial strategy of high-throughput virtual screening and kinase activity prediction, and identified AH057 as a novel JAK1/2 inhibitor that blocks interleukin 6 (IL-6)-, interferon-α (IFN-α)-, and interferon-γ (IFN-γ)-induced JAK1, JAK2, and protein-tyrosine kinase 2 (TYK2) phosphorylation and the corresponding STAT1 and STAT3 phosphorylation.…”

Section: Introductionmentioning

confidence: 99%

Combined inhibition of JAK1/2 and DNMT1 by newly identified small-molecule compounds synergistically suppresses the survival and proliferation of cervical cancer cells

et al. 2020

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Despite substantial advances in treating cervical cancer (CC) with surgery, radiation and chemotherapy, patients with advanced CC still have poor prognosis and significantly variable clinical outcomes due to tumor recurrence and metastasis. Therefore, to develop more efficacious and specific treatments for CC remains an unmet clinical need. In this study, by virtual screening the SPECS database, we identified multiple novel JAK inhibitor candidates and validated their antitumor drug efficacies that were particularly high against CC cell lines. AH057, the best JAK inhibitor identified, effectively blocked the JAK/STAT pathways by directly inhibiting JAK1/2 kinase activities, and led to compromised cell proliferation and invasion, increased apoptosis, arrested cell cycles, and impaired tumor progression in vitro and in vivo. Next, by screening the Selleck chemical library, we identified SGI-1027, a DNMT1 inhibitor, as the compound that displayed the highest synergy with AH057. By acting on a same set of downstream effector molecules that are dually controlled by JAK1/2 and DNMT1, the combination of AH057 with SGI-1027 potently and synergistically impaired CC cell propagation via dramatically increasing apoptotic cell death and cell-cycle arrest. These findings establish a preclinical proof of concept for combating CC by dual targeting of JAK1/2 and DNMT1, and provide support for launching a clinical trial to evaluate the efficacy and safety of this drug combination in patients with CC and other malignant tumors.

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Force Fields for Small Molecules

Cited by 41 publications

References 223 publications

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials

Combined inhibition of JAK1/2 and DNMT1 by newly identified small-molecule compounds synergistically suppresses the survival and proliferation of cervical cancer cells

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