Binding Affinity Prediction with Property-Encoded Shape Distribution Signatures

Das, Sourav; Krein, Michael; Breneman, Curt M.

doi:10.1021/ci9004139

Cited by 61 publications

(56 citation statements)

References 67 publications

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“…Empirical Knowledge-Based DOCK [32] AutoDock [34] SMoG [82] AutoDock [34] GlideScore [37] DrugScore [62] GoldScore [35] ChemScore [60] PMF_Score [83] ICM [46] X_Score [66] MotifScore [84] LigandFit [47] F_Score [73] RF_Score [85] Molegro Virtual Docker [48] Fresno [75] PESD_SVM [86] SYBYL_G-Score [73] SCORE [76] PoseScore [87] SYBYL_D-Score [73] LUDI [77] MedusaScore [74] SFCscore [78] HYDE [79] LigScore [80] PLP [81] …”

Section: Force-field-basedmentioning

confidence: 99%

Molecular Docking and Structure-Based Drug Design Strategies

et al. 2015

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Pharmaceutical research has successfully incorporated a wealth of molecular modeling methods, within a variety of drug discovery programs, to study complex biological and chemical systems. The integration of computational and experimental strategies has been of great value in the identification and development of novel promising compounds. Broadly used in modern drug design, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets. This approach also estimates the ligand-receptor binding free energy by evaluating critical phenomena involved in the intermolecular recognition process. Today, as a variety of docking algorithms are available, an understanding of the advantages and limitations of each method is of fundamental importance in the development of effective strategies and the generation of relevant results. The purpose of this review is to examine current molecular docking strategies used in drug discovery and medicinal chemistry, exploring the advances in the field and the role played by the integration of structure-and ligand-based methods.

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Section: Force-field-basedmentioning

confidence: 99%

Molecular Docking and Structure-Based Drug Design Strategies

et al. 2015

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“…The PEST descriptors have proven very competitive in several models: prediction of protein HPLC retention times, glass transition temperatures of proteins, HIV reverse transcriptase ligand-based QSAR, clustering of protein binding sites and prediction of ligand-binding affinities [38][39][40][41].…”

Section: Pest Methodologymentioning

confidence: 99%

New Types of Descriptors and Models in QSAR/QSPR

Krämer

Clark

2012

Statistical Modelling of Molecular Descriptors in QSAR/QSPR

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In this chapter, we will concentrate on novel information-rich descriptors intended not only for quantitative structure-property relationships (QSPRs), but also for quantitative structure-activity relationships (QSARs). We will concentrate on descriptors that are derived from quantum mechanical calculations and that do not rely heavily on the 2D molecular structure. Many such descriptors are derived from local properties calculated at a molecular surface, so that we can consider two main aspects: the local properties themselves and the techniques used to derive descriptors from them. The goal of just about all QSARs and QSPRs is to describe intermolecular interactions. We, therefore, need local properties that describe the individual components of these interactions. These have been reviewed in detail [1], but will be described here once more in order to set the scene for the individual types of descriptors.Intermolecular interactions are conventionally described in terms of four components [2]: 1) Pauli (electron-electron) repulsion 2) Coulomb (electrostatic) interactions 3) Dispersion (van der Waals) attraction 4) Donor-acceptor (Lewis acid-base) interactions.In conventional classical treatments of molecules (e.g., in force fields and similar treatments for docking, scoring, QSAR, etc.), the Pauli repulsion and the dispersion interaction are combined into a single Lennard-Jones [3] or Buckingham [4] potential, although soft-core variations (i.e., ones with a less steep rise in the Pauli repulsion term) are now becoming popular for free-energy perturbation calculations [5]. Coulomb interactions are generally considered to play a dominant role in determining intermolecular interactions because of their strength in the gas phase and their long range. Their importance decreases in polar solutions, but QSAR and QSPR have Statistical Modelling of Molecular Descriptors in QSAR/QSPR. First Edition. Edited

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“…By the a priori removal of sequences with low chemical diversity, we aim to identify sequences that bind keratin by true affi nity, hence eliminating sequences that permeate through nonspecifi c interactions. [ 11 ] Further, by reducing the number of hydrophobic (either aliphatic or aromatic) amino acids, we lower the probability of identifying SPPs with poor water solubility. Similarly, by reducing the number of charged amino acids per sequence we aim to identify sequences with lower risk of eliciting skin irritation, as is the case of poly-R.…”

Section: Selection Of Spps For Csa Delivery Via In Silico Library Scrmentioning

confidence: 98%

De Novo Design of Skin‐Penetrating Peptides for Enhanced Transdermal Delivery of Peptide Drugs

Menegatti

Zakrewsky

Kumar

et al. 2016

Adv Healthcare Materials

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Skin-penetrating peptides (SPPs) are attracting increasing attention as a non-invasive strategy for transdermal delivery of therapeutics. The identification of SPP sequences, however, currently performed by experimental screening of peptide libraries, is very laborious. Recent studies have shown that, to be effective enhancers, SPPs must possess affinity for both skin keratin and the drug of interest. We therefore developed a computational process for generating and screening virtual libraries of disulfide-cyclic peptides against keratin and cyclosporine A (CsA) to identify SPPs capable of enhancing transdermal CsA delivery. The selected sequences were experimentally tested and found to bind both CsA and keratin, as determined by mass spectrometry and affinity chromatography, and enhance transdermal permeation of CsA. Four heptameric sequences that emerged as leading candidates (ACSATLQHSCG, ACSLTVNWNCG, ACTSTGRNACG, and ACSASTNHNCG) were tested and yielded CsA permeation on par with previously identified SPP SPACE (TM) . An octameric peptide (ACNAHQARSTCG) yielded significantly higher delivery of CsA compared to heptameric SPPs. The safety profile of the selected sequences was also validated by incubation with skin keratinocytes. This method thus represents an effective procedure for the de novo design of skin-penetrating peptides for the delivery of desired therapeutic or cosmetic agents.

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Binding Affinity Prediction with Property-Encoded Shape Distribution Signatures

Cited by 61 publications

References 67 publications

Molecular Docking and Structure-Based Drug Design Strategies

Molecular Docking and Structure-Based Drug Design Strategies

New Types of Descriptors and Models in QSAR/QSPR

De Novo Design of Skin‐Penetrating Peptides for Enhanced Transdermal Delivery of Peptide Drugs

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