Number Of Hydrogen Bond Donors Research Articles

Design, Synthesis, and Anti-HIV-1 Evaluation of a Novel Series of 1,2,3,4-Tetrahydropyrimidine-5-Carboxylic Acid Derivatives.

A series of tetrahydropyrimidine derivatives (2a - 2l) were designed, synthesized, and screened for anti-HIV-1 properties based on the structures of HIV-1 gp41 binding site inhibitors, NB-2 and NB-64. A computational study was performed to predict the pharmacodynamics, pharmacokinetics, and drug-likeness features of the studied molecules. Docking studies revealed that the carboxylic acid group in the molecules forms salt bridges with either Lys574 or Arg579. Physiochemical properties (e.g., molecular weight, number of hydrogen bond donors, number of hydrogen bond acceptors, and number of rotatable bonds) of the synthesized compounds confirmed and exhibited that these compounds were within the range set by Lipinski's rule of five. Compounds 2e and 2k with 4-chlorophenyl substituent and 4-methylphenyl group at C(4) position of the tetrahydropyrimidine ring was the most potent one among the tested compounds. This suggests that these compounds may serve as leads for development of novel small-molecule HIV-1 inhibitors.

Aminobenzisoxazole compounds as agonists of α7 nicotinic acetylcholine receptors: a patent evaluation (WO 2017027600)

ABSTRACTIntroduction: alpha 7 subtype nicotinic acetylcholine receptor (α7 nAChR) ligands, that is, ligands that interact with the orthosteric or allosteric binding sites of α7 nAChR, hold great potential for several therapeutic applications. Numerous compounds have been designed targeting α7 nAChR but most of them cannot be used therapeutically for various reasons.Areas covered: The patent application describes a series of germinal substituted aminobenzisoxazole compounds as α7 nAChR ligands. These compounds were claimed as potential therapeutics for treating and/or improving cognitive function. All of the (R)-stereoisomer presented high binding activities for α7 nAChR and several compounds displayed excellent selectivity over 5-HT3R.Expert opinion: The privileged structure-derived modification via bioisosterism and scaffold hopping is an important approach for seeking novel α7 nAChR ligands. The claimed germinal substituted aminobenzisoxazole derivatives with low tPSA values as well as low number of hydrogen bond donors and acceptors are supposed to have sufficient BBB penetration. Although there is a lack of essential biological data and the molecular mechanisms are not clear, these compounds stand for a new type of α7 nAChR ligands and deserve further studies.

Multifunctional Organosulfonate Anions Self-Assembled with Organic Cations by Charge-Assisted Hydrogen Bonds and the Cooperation of Water

The present study focuses on the assembly of organo-cations with organo-anions in water. The anions, characterized by symmetric moieties (carbon-, adamantane-, or calixarene-based) functionalized with directional hydrogen bond (HB) acceptor functions (tetra-sulfonate moieties), are combined with planar guanidinium or terephtalimidamide cations as hydrogen bond donors, the purpose being to integrate water molecules into the lattice. The imbalance between the charge on the two components, and the considerable number of HB donor and acceptor sites, promotes the insertion of water into the structures. In the reported structures, a part of the water molecules serves as a structural linker between the anions and cations, while the remaining molecules cluster into channels and cavities in a loose association with the supramolecular matrix framework.

SMMDB: a web-accessible database for small molecule modulators and their targets involved in neurological diseases.

High-throughput screening and better understanding of small molecule’s structure–activity relationship (SAR) using computational biology techniques have greatly expanded the face of drug discovery process in better discovery of therapeutics for various disease. Small Molecule Modulators Database (SMMDB) includes >1100 small molecules that have been either approved by US Food and Drug Administration, are under investigation or were rejected in clinical trial for any kind of neurological diseases. The comprehensive information about small molecules includes the details about their molecular targets (such as protein or enzyme, DNA, RNA, antisense RNA etc.), pharmacokinetic and pharmacodynamic properties such as binding affinity to their targets (Kd, Ki, IC50 and EC50 if available), mode of action, log P-value, number of hydrogen bond donor and acceptors, their clinical trial status, their 2D and three-dimensional structures etc. To enrich the basic annotation of every small molecule entry present in SMMDB, it is hyperlinked to their description present in PubChem, DrugBank, PubMed and KEGG database. The annotation about their molecular targets was enriched by linking it with UniProt and GenBank and STRING database that can be utilized to study the interaction and relation between various targets involved in single neurological disease. All molecules present in the SMMDB are made available to download in single file and can be further used in establishing the SAR, structure-based drug designing as well as shape-based virtual screening for developing the novel therapeutics against neurological diseases. The scope of this database majorly covers the interest of scientific community and researchers who are engaged in putting their endeavor toward therapeutic development and investigating the pathogenic mechanism of various neurological diseases. The graphical user interface of the SMMDB is accessible on http://bsbe.iiti.ac.in/bsbe/smmdb.

Classification of medicines using naive bayes classifier

In data mining there is a function called classification that assigns items into groups based on their similarities. These classification techniques are used in many real time applications to classify the data. In pharmaceutical industries all the new medicines need to get approval from Food and Drug administration (FDA) before bringing them into the market. FDA is completely depended on its manual operation and leads to more amount of time for the approval of new medicines. By using classification technique the FDA can automate the procedure with which the time required for approval of new medicine can be reduced. For classification of unknown medicines six chemical properties are used such as relative molecular mass, Number of hydrogen bond donor, Number of hydrogen bond acceptor, Polar surface Area , Hydrophobic constant and number of flexible rotation keys. Drug category and these six properties are collected from U.S National Library of Medicine for creating a new database. By applying Naive Bayes algorithm on the collected data, target class of unknown medicines can be accurately predicted. The fundamental purpose of this paper is to provide a comprehensive analysis of classification methods for predicting unknown medicines. And even discusses the computation procedure of binary classification system for fever and typhoid medicines.

Insights into the Molecular Basis of the Acute Contact Toxicity of Diverse Organic Chemicals in the Honey Bee.

Use of chemical pollutants, including pesticides and other industrial chemicals, has resulted in significant risks to the whole ecosystem. Therefore, ecological risk assessment of chemicals is vital and necessary. Since the honey bee (Apis mellifera) is probably among the most exposed species to the polluting chemicals, we focused on the in silico estimation of honey bee toxicity (HBT) of chemicals and the analysis of the relevance of chemical HBT and several key physical-chemical properties and structural characteristics. A total of 40 classification models were developed by combination of five machine learning methods along with seven kinds of fingerprints and a set of molecular descriptors. After 5-fold cross validation and external validation, several models showed good predictive power. The relevance of 12 key physical-chemical properties and chemical HBT was also investigated. Five properties, including AlogP, logD, molecular weight (MW), molecular surface area (MSA), and the number of rotatable bonds (nRTB), indicated positive correlation coefficients with HBT, while molecular solubility (logS) and the number of hydrogen bond donors (nHBD) indicated negative correlation coefficients. Finally, seven privileged substructures responsible for chemical HBT were identified from KRFP and SubFP fingerprints. The results of this study should provide critical information and useful tools for chemical HBT estimation in environmental risk assessment.

In silico repurposing of antipsychotic drugs for Alzheimer\u2019s disease

BackgroundAlzheimer’s disease (AD) is the most prevalent form of dementia and represents one of the highest unmet requirements in medicine today. There is shortage of novel molecules entering into market because of poor pharmacokinetic properties and safety issues. Drug repurposing offers an opportunity to reinvigorate the slowing drug discovery process by finding new uses for existing drugs. The major advantage of the drug repurposing approach is that the safety issues are already investigated in the clinical trials and the drugs are commercially available in the marketplace. As this approach provides an effective solution to hasten the process of providing new alternative drugs for AD, the current study shows the molecular interaction of already known antipsychotic drugs with the different protein targets implicated in AD using in silico studies.ResultA computational method based on ligand–protein interaction was adopted in present study to explore potential antipsychotic drugs for the treatment of AD. The screening of approximately 150 antipsychotic drugs was performed on five major protein targets (AChE, BuChE, BACE 1, MAO and NMDA) by molecular docking. In this study, for each protein target, the best drug was identified on the basis of dock score and glide energy. The top hits were then compared with the already known inhibitor of the respective proteins. Some of the drugs showed relatively better docking score and binding energies as compared to the already known inhibitors of the respective targets. Molecular descriptors like molecular weight, number of hydrogen bond donors, acceptors, predicted octanol/water partition coefficient and percentage human oral absorption were also analysed to determine the in silico ADME properties of these drugs and all were found in the acceptable range and follows Lipinski’s rule.ConclusionThe present study have led to unravel the potential of leading antipsychotic drugs such as pimozide, bromperidol, melperone, anisoperidone, benperidol and anisopirol against multiple targets associated with AD. Benperidol was found to be the best candidate drug interacting with different target proteins involved in AD.

Matched Molecular Pair Analysis on Large Melting Point Datasets: A Big Data Perspective.

A matched molecular pair (MMP) analysis was used to examine the change in melting point (MP) between pairs of similar molecules in a set of ∼275k compounds. We found many cases in which the change in MP (ΔMP) of compounds correlates with changes in functional groups. In line with the results of a previous study, correlations between ΔMP and simple molecular descriptors, such as the number of hydrogen bond donors, were identified. In using a larger dataset, covering a wider chemical space and range of melting points, we observed that this method remains stable and scales well with larger datasets. This MMP‐based method could find use as a simple privacy‐preserving technique to analyze large proprietary databases and share findings between participating research groups.

Probing the disparate effects of arginine and lysine residues on antimicrobial peptide/bilayer association

Antimicrobial peptides (AMPs) are key components of the innate immune response and represent promising templates for the development of broad-spectrum alternatives to conventional antibiotics. Most AMPs are short, cationic peptides that interact more strongly with negatively charged prokaryotic membranes than net neutral eukaryotic ones. Both AMPs and synthetic analogues with arginine-like side chains are more active against bacteria than those with lysine-like amine groups, though the atomistic mechanism for this increase in potency remains unclear. To examine this, we conducted comparative molecular dynamics simulations of a model negatively-charged membrane system interacting with two mutants of the AMP KR-12: one with lysine residues mutated to arginines (R-KR12) and one with arginine residues mutated to lysine (K-KR12). Simulations show that both partition analogously to the bilayer and display similar preferences for hydrogen bonding with the anionic POPGs. However, R-KR12 binds stronger to the bilayer than K-KR12 and forms significantly more hydrogen bonds, leading to considerably longer interaction times. Additional simulations with methylated R-KR12 and charge-modified K-KR12 mutants show that the extensive interaction seen in the R-KR12 system is partly due to arginine's strong atomic charge distribution, rather than being purely an effect of the greater number of hydrogen bond donors. Finally, free energy simulations reveal that both peptides are disordered in solution but form an amphipathic α-helix when inserted into the bilayer headgroup region. Overall, these results highlight the role of charge and hydrogen bond strength in peptide bilayer insertion, and offer potential insights for designing more potent analogues in the future.

Molecular Properties and Bio-Activity Score of 2{[2-(4-chlorophenyl)-4- oxoquinazolin-3(4H)-yl]amino}-N-(substitutedphenyl) Acetamides

Purpose of the paper : Quinazolinone derivatives have been found to possess a wide range of biological activity, a series of 2{[2-(4-chlorophenyl)-4-oxoquinazolin-3(4H)-yl]amino}-N-(substitutedphenyl) acetamides have been taken to study the molecular properties and bio-activity score that shall aid in designing newer compounds. Design/methodology/approach : Molinspiration software was used for the calculation of Mi Log P values and all other biological data of the compounds. Findings : It was found that the values were in the range of 5 that means these compounds have good permeability across cell membrane. TPSA in the range of 76.02 to 121.85 (well below 160) and molecular mass<500. Number of violations is 0 and rotb < 7. Number of hydrogen bond donors <5 (The sum of OHs and NHs) and hydrogenbond acceptor < 7 (The sum of Os and Ns). These observation showed that the compounds can easily bind to receptor and weretaken further for the calculation of bioactivity score. Research limitations/implications : The result of bioactivity score of GPCR ligand, ion channel modulator,nuclear receptor ligand, inhibitor activities towards enzymes, protease and kinase showed that the compounds exhibit moderate score towards all the receptors. Hence, these compounds shall be taken for further studies to evaluate their biological profile. Practical implications : A series of similar analogs shall be considered for evaluating the bioactivity score and compared for designing newer compounds. Social Implications : Designing of newer derivatives based on the bioactivity score of these reported compounds shall have profound biological activity. What is original/value of paper : The article deals with evaluation of bioactivity score of a series of quinazolinone derivatives.

Mode of Action Analyses of Neferine, a Bisbenzylisoquinoline Alkaloid of Lotus (Nelumbo nucifera) against Multidrug-Resistant Tumor Cells.

Neferine, a bisbenzylisoquinoline alkaloid isolated from the green seed embryos of Lotus (Nelumbo nucifera Gaertn), has been previously shown to have various anti-cancer effects. In the present study, we evaluated the effect of neferine in terms of P-glycoprotein (P-gp) inhibition via in vitro cytotoxicity assays, R123 uptake assays in drug-resistant cancer cells, in silico molecular docking analysis on human P-gp and in silico absorption, distribution, metabolism, and excretion (ADME), quantitative structure activity relationships (QSAR) and toxicity analyses. Lipinski rule of five were mainly considered for the ADME evaluation and the preset descriptors including number of hydrogen bond donor, acceptor, hERG IC50, logp, logD were considered for the QSAR analyses. Neferine revealed higher toxicity toward paclitaxel- and doxorubicin-resistant breast, lung or colon cancer cells, implying collateral sensitivity of these cells toward neferine. Increased R123 uptake was observed in a comparable manner to the control P-gp inhibitor, verapamil. Molecular docking analyses revealed that neferine still interacts with P-gp, even if R123 was pre-bound. Bioinformatical ADME and toxicity analyses revealed that neferine possesses the druggability parameters with no predicted toxicity. In conclusion, neferine may allocate the P-gp drug-binding pocket and prevent R123 binding in agreement with P-gp inhibition experiments, where neferine increased R123 uptake.

Synthesis of C-Ribosyl-1,2,3-triazolyl Carboxamides

Because of the emergence of new viruses, the need for new antiviral broad-spectrum compounds remains important. In this context, herein the synthesis of C-nucleosides, structurally close to ribavirin, a nucleoside presenting various biological activities and used until now particularly for its broad-spectrum antiviral properties, is reported. The compounds were designed in order to increase their stability and the number of hydrogen bond donor or acceptor in comparison to ribavirin, and to investigate the role of the carboxamide group on the biological activity. The efficient synthesis of 11 C-nucleosides is based on an indium-mediated alkynylglycosylation as the key step, followed by the construction of the triazole heterocycle. Amidation was performed with primary and secondary amines in yields up to 85%. An analogue nucleoside with a triazole without carboxamide group was also prepared in order to compare its activity. Finally, the carboxamide group was moved to the N-1 triazole position to mimic ribavirin.

High-Throughput Screening and Prediction Model Building for Novel Hemozoin Inhibitors Using Physicochemical Properties.

It is essential to continue the search for novel antimalarial drugs due to the current spread of resistance against artemisinin by Plasmodium falciparum parasites. In this study, we developed in silico models to predict hemozoin inhibitors as a potential first-step screening for novel antimalarials. An in vitro colorimetric high-throughput screening assay of hemozoin formation was used to identify hemozoin inhibitors from 9,600 structurally diverse compounds. The physicochemical properties of positive hits and randomly selected compounds were extracted from the ChemSpider database; they were used for developing prediction models to predict hemozoin inhibitors using two different approaches, i.e., traditional multivariate logistic regression and Bayesian model averaging. Our results showed that a total of 224 positive-hit compounds exhibited the ability to inhibit hemozoin formation, with 50% inhibitory concentrations (IC50s) ranging from 3.1 μM to 199.5 μM. The best model according to traditional multivariate logistic regression included the three variables octanol-water partition coefficient, number of hydrogen bond donors, and number of atoms of hydrogen, while the best model according to Bayesian model averaging included the three variables octanol-water partition coefficient, number of hydrogen bond donors, and index of refraction. Both models had a good discriminatory power, with area under the curve values of 0.736 and 0.781 for the traditional multivariate model and Bayesian model averaging, respectively. In conclusion, the prediction models can be a new, useful, and cost-effective approach for the first screen of hemozoin inhibition-based antimalarial drug discovery.

The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood-brain barrier (BBB) permeability.

Drugs acting on central nervous system (CNS) may take longer duration to reach the market as these compounds have a higher attrition rate in clinical trials due to the complexity of the brain, side effects, and poor blood-brain barrier (BBB) permeability compared to non-CNS-acting compounds. The roles of active efflux transporters with BBB are still unclear. The aim of the present work was to develop a predictive model for BBB permeability that includes the MRP-1 transporter, which is considered as an active efflux transporter. A support vector machine model was developed for the classification of MRP-1 substrates and non-substrates, which was validated with an external data set and Y-randomization method. An artificial neural network model has been developed to evaluate the role of MRP-1 on BBB permeation. A total of nine descriptors were selected, which included molecular weight, topological polar surface area, ClogP, number of hydrogen bond donors, number of hydrogen bond acceptors, number of rotatable bonds, P-gp, BCRP, and MRP-1 substrate probabilities for model development. We identified 5 molecules that fulfilled all criteria required for passive permeation of BBB, but they all have a low logBB value, which suggested that the molecules were effluxed by the MRP-1 transporter.

Defining desirable natural product derived anticancer drug space: optimization of molecular physicochemical properties and ADMET attributes

&lt;p class="ADMETabstracttext"&gt;As part of our endeavor to enhance survival of natural product derived drug candidates and to guide the medicinal chemist to design higher probability space for success in the anti cancer drug development area, we embarked on a detailed study of the property space for a collection of natural product derived anti cancer molecules. We carried out a comprehensive analysis of properties for 24 natural products derived anti cancer drugs including clinical development candidates and a set of 27 natural products derived anti cancer lead compounds. In particular, we focused on understanding the interplay among eight physicochemical properties including like partition coefficient (log P), distribution coefficient at pH=7.4 (log D), topological polar surface area (TPSA), molecular weight (MW), aqueous solubility (log S), number of hydrogen bond acceptors (HBA), number of hydrogen bond donors (HBD) and number of rotatable bonds (n&lt;sub&gt;Rot&lt;/sub&gt;) crucial for drug design and relationships between physicochemical properties, ADME (absorption, distribution, metabolism, and elimination) attributes, and in silico toxicity profile for these two sets of compounds. This analysis provides guidance for the chemist to modify the existing natural product scaffold or designing of new anti cancer molecules in a property space with increased probability of success and may lead to the identification of druglike candidates with favorable safety profiles that can successfully test hypotheses in the clinic.&lt;/p&gt;

In silico receptor-based drug design of X,Y-benzenesulfonamide derivatives as selective COX-2 inhibitors

COX-2 is a widely studied biological target, since its activity is directly related to the inflammation response. The design of COX-2 selective inhibitors is an ongoing topic in drug design. We performed a quantitative structure–activity relationship and docking studies over a series of benzenesulfonamide derivatives on their inhibition towards COX-1 and COX-2, in order to rationalize their selectivity towards COX-2. Constitutional, topological and molecular property descriptors for the QSAR models and molecular docking calculations were employed. The mathematical model highlighted that lipophilic character and size are the most important features for COX-2 inhibition by benzenesulfonamides. A second QSAR model revealed that the dipole moment, the number of hydrogen bond donors and lipophilicity descriptors of benzenesulfonamides are crucial for their binding to COX-1. Moreover, artificial neural networks were employed to improve the prediction power of the COX-1 inhibition QSAR model. In this sense, we proposed new selective potential inhibitors by introducing different halogens into the benzenesulfonamide scaffold, improving their interactions with key residues of COX-2.

Systematic N-methylation of oxytocin: Impact on pharmacology and intramolecular hydrogen bonding network

Oxytocin (OT) is a peptide hormone agonist of the OT receptor (OTR) that plays an important role in social behaviors such as pair bonding, maternal bonding and trust. The pharmaceutical development of OT as an oral peptide therapeutic has been hindered historically by its unfavorable physicochemical properties, including molecular weight, polarity and number of hydrogen bond donors, which determines poor cell permeability. Here we describe the first systematic study of single and multiple N-methylations of OT and their effect on physicochemical properties as well as potency at the OT receptor. The agonist EC50 and percent effect for OTR are reported and show that most N-methylations are tolerated but with some loss in potency compared to OT. The effect of N-methylation on exposed polarity is assessed through the EPSA chromatographic method and the results validated against NMR temperature coefficient experiments and the determination of NMR solution structures. We found that backbone methylation of residues not involved in IMHB and removal of the N-terminal amine can significantly reduce the exposed polarity of peptides, and yet retain a significant OTR agonist activity. The results of this study also expose the potential challenge of using the N-methylation strategy for the OT system; while exposed polarity is reduced, in some cases backbone methylation produces a significant conformational change that compromises agonist activity. The data presented provides useful insights on the SAR of OT and suggests future design strategies that can be used to develop more permeable OTR agonists based on the OT framework.

Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs.

BackgroundA large number of natural products have shown in vitro antiplasmodial activities. Early identification and prioritization of these natural products with potential for novel mechanism of action, desirable pharmacokinetics and likelihood for development into drugs is advantageous. Chemo-informatic profiling of these natural products were conducted and compared to currently registered anti-malarial drugs (CRAD).MethodsNatural products with in vitro antiplasmodial activities (NAA) were compiled from various sources. These natural products were sub-divided into four groups based on inhibitory concentration (IC50). Key molecular descriptors and physicochemical properties were computed for these compounds and analysis of variance used to assess statistical significance amongst the sets of compounds. Molecular similarity analysis, estimation of drug-likeness, in silico pharmacokinetic profiling, and exploration of structure–activity landscape were also carried out on these sets of compounds.ResultsA total of 1040 natural products were selected and a total of 13 molecular descriptors were analysed. Significant differences were observed among the sub-groups of NAA and CRAD for at least 11 of the molecular descriptors, including number of hydrogen bond donors and acceptors, molecular weight, polar and hydrophobic surface areas, chiral centres, oxygen and nitrogen atoms, and shape index. The remaining molecular descriptors, including clogP, number of rotatable bonds and number of aromatic rings, did not show any significant difference when comparing the two compound sets. Molecular similarity and chemical space analysis identified natural products that were structurally diverse from CRAD. Prediction of the pharmacokinetic properties and drug-likeness of these natural products identified over 50 % with desirable drug-like properties. Nearly 70 % of all natural products were identified as potentially promiscuous compounds. Structure–activity landscape analysis highlighted compound pairs that form ‘activity cliffs’. In all, prioritization strategies for the NAA were proposed.ConclusionsChemo-informatic profiling of NAA and CRAD have produced a wealth of information that may guide decisions and facilitate anti-malarial drug development from natural products. Articulation of the information provided within an interactive data-mining environment led to a prioritized list of NAA.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1087-y) contains supplementary material, which is available to authorized users.

NALDB: nucleic acid ligand database for small molecules targeting nucleic acid.

Nucleic acid ligand database (NALDB) is a unique database that provides detailed information about the experimental data of small molecules that were reported to target several types of nucleic acid structures. NALDB is the first ligand database that contains ligand information for all type of nucleic acid. NALDB contains more than 3500 ligand entries with detailed pharmacokinetic and pharmacodynamic information such as target name, target sequence, ligand 2D/3D structure, SMILES, molecular formula, molecular weight, net-formal charge, AlogP, number of rings, number of hydrogen bond donor and acceptor, potential energy along with their Ki, Kd, IC50 values. All these details at single platform would be helpful for the development and betterment of novel ligands targeting nucleic acids that could serve as a potential target in different diseases including cancers and neurological disorders. With maximum 255 conformers for each ligand entry, our database is a multi-conformer database and can facilitate the virtual screening process. NALDB provides powerful web-based search tools that make database searching efficient and simplified using option for text as well as for structure query. NALDB also provides multi-dimensional advanced search tool which can screen the database molecules on the basis of molecular properties of ligand provided by database users. A 3D structure visualization tool has also been included for 3D structure representation of ligands. NALDB offers an inclusive pharmacological information and the structurally flexible set of small molecules with their three-dimensional conformers that can accelerate the virtual screening and other modeling processes and eventually complement the nucleic acid-based drug discovery research. NALDB can be routinely updated and freely available on bsbe.iiti.ac.in/bsbe/naldb/HOME.php.Database URL: http://bsbe.iiti.ac.in/bsbe/naldb/HOME.php

Long-Term Amorphous Drug Stability Predictions Using Easily Calculated, Predicted, and Measured Parameters.

The purpose of this study was to develop a predictive model of the amorphous stability of drugs with particular relevance for poorly water-soluble compounds. Twenty-five representative neutral poorly soluble compounds with a diverse range of physicochemical properties and chemical structures were systematically selected from an extensive library of marketed drug products. The physical stability of the amorphous form, measured over a 6 month period by the onset of crystallization of amorphous films prepared by melting and quench-cooling, was assessed using polarized light microscopy. The data were used as a response variable in a statistical model with calculated/predicted or measured molecular, thermodynamic, and kinetic parameters as explanatory variables. Several multiple linear regression models were derived, with varying balance between calculated/predicted and measured parameters. It was shown that inclusion of measured parameters significantly improves the predictive ability of the model. The best model demonstrated a prediction accuracy of 82% and included the following as parameters: melting and glass transition temperatures, enthalpy of fusion, configurational free energy, relaxation time, number of hydrogen bond donors, lipophilicity, and the ratio of carbon to heteroatoms. Good predictions were also obtained with a simpler model, which was comprised of easily acquired quantities: molecular weight and enthalpy of fusion. Statistical models are proposed to predict long-term amorphous drug stability. The models include readily accessible parameters, which are potentially the key factors influencing amorphous stability. The derived models can support faster decision making in drug formulation development.