Ligand-Based Compound Activity Prediction via Few-Shot Learning.

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a "few-shot learning" challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets. The code for FS-CAP is available at https://github.com/Rose-STL-Lab/FS-CAP.

Is the 21st birthday a turning point for alcohol and cannabis use? A monthly study of young adults.

BACKGROUND: An important life-course event with respect to alcohol and cannabis use is turning 21 years of age, which may be associated with increases in use of these substances due to celebrations during the month and easier access to them on and following this birthday. We examined the trajectories of alcohol and cannabis use behaviors in the months leading up to, during, and following the 21st birthday month. We also examined whether the use trajectories vary by college status and baseline levels of use. METHODS: We used data from 203 young adults recruited from the Greater Seattle region who turned 21 during the course of the study. Surveys were administered each month for 24 consecutive months. Measures included the typical number of drinks per week for the past month, the frequency of heavy episodic drinking, the number of cannabis use days, and any simultaneous alcohol and cannabis use. Multilevel spline models were run that estimated linear slopes over time at four intervals: (1) up to 1 month before the 21st birthday month; (2) from 1 month before to the month of the 21st birthday; (3) from the 21st birthday month to 1 month following; and (4) from 1 month following the 21st birthday month through all following months. RESULTS: Alcohol use, generally, and simultaneous alcohol and cannabis use showed sharp increases from the month before the 21st birthday month to the 21st birthday month and decreases following the 21st birthday month. For cannabis use, there were significant increases in the months leading up to the 21st birthday and no other significant changes during other time intervals. Patterns differed by baseline substance use and college status. CONCLUSIONS: Findings from the current study have implications for the timing and personalization of prevention and intervention efforts. Event-specific 21st birthday interventions may benefit from incorporating content targeting specific hazardous drinking behaviors in the month prior to the 21st birthday.

Free Energy Density of a Fluid and Its Role in Solvation and Binding.

The concept that a fluid has a position-dependent free energy density appears in the literature but has not been fully developed or accepted. We set this concept on an unambiguous theoretical footing via the following strategy. First, we set forth four desiderata that should be satisfied by any definition of the position-dependent free energy density, f(R), in a system comprising only a fluid and a rigid solute: its volume integral, plus the fixed internal energy of the solute, should be the system free energy; it deviates from its bulk value, fbulk, near a solute but should asymptotically approach fbulk with increasing distance from the solute; it should go to zero where the solvent density goes to zero; and it should be well-defined in the most general case of a fluid made up of flexible molecules with an arbitrary interaction potential. Second, we use statistical thermodynamics to formulate a definition of the free energy density that satisfies these desiderata. Third, we show how any free energy density satisfying the desiderata may be used to analyze molecular processes in solution. In particular, because the spatial integral of f(R) equals the free energy of the system, it can be used to compute free energy changes that result from the rearrangement of solutes as well as the forces exerted on the solutes by the solvent. This enables the use of a thermodynamic analysis of water in protein binding sites to inform ligand design. Finally, we discuss related literature and address published concerns regarding the thermodynamic plausibility of a position-dependent free energy density. The theory presented here has applications in theoretical and computational chemistry and may be further generalizable beyond fluids, such as to solids and macromolecules.

A Fast, Convenient, Polarizable Electrostatic Model for Molecular Dynamics.

We present an efficient polarizable electrostatic model, utilizing typed, atom-centered polarizabilities and the fast direct approximation, designed for efficient use in molecular dynamics (MD) simulations. The model provides two convenient approaches for assigning partial charges in the context of atomic polarizabilities. One is a generalization of RESP, called RESP-dPol, and the other, AM1-BCC-dPol, is an adaptation of the widely used AM1-BCC method. Both are designed to accurately replicate gas-phase quantum mechanical electrostatic potentials. Benchmarks of this polarizable electrostatic model against gas-phase dipole moments, molecular polarizabilities, bulk liquid densities, and static dielectric constants of organic liquids show good agreement with the reference values. Of note, the model yields markedly more accurate dielectric constants of organic liquids, relative to a matched nonpolarizable force field. MD simulations with this method, which is currently parametrized for molecules containing elements C, N, O, and H, run only about 3.6-fold slower than fixed charge force fields, while simulations with the self-consistent mutual polarization average 4.5-fold slower. Our results suggest that RESP-dPol and AM1-BCC-dPol afford improved accuracy relative to fixed charge force fields and are good starting points for developing general, affordable, and transferable polarizable force fields. The software implementing these approaches has been designed to utilize the force field fitting frameworks developed and maintained by the Open Force Field Initiative, setting the stage for further exploration of this approach to polarizable force field development.

Host-guest systems for the SAMPL9 blinded prediction challenge: phenothiazine as a privileged scaffold for binding to cyclodextrins.

Model systems are widely used in biology and chemistry to gain insight into more complex systems. In the field of computational chemistry, researchers use host-guest systems, relatively simple exemplars of noncovalent binding, to train and test the computational methods used in drug discovery. Indeed, host-guest systems have been developed to support the community-wide blinded SAMPL prediction challenges for over a decade. While seeking new host-guest systems for the recent SAMPL9 binding prediction challenge, which is the focus of the present PCCP Themed Collection, we identified phenothiazine as a privileged scaffold for guests of ß cyclodextrin (ßCD) and its derivatives. Building on this observation, we used calorimetry and NMR spectroscopy to characterize the noncovalent association of native ßCD and three methylated derivatives of ßCD with five phenothiazine drugs. The strongest association observed, that of thioridazine and one of the methyl derivatives, exceeds the well-known high affinity of rimantidine with ßCD. Intriguingly, however, methylation of ßCD at the 3 position abolished detectible binding for all of the drugs studied. The dataset has a clear pattern of entropy-enthalpy compensation. The NMR data show that all of the drugs position at least one aromatic proton at the secondary face of the CD, and most also show evidence of deep penetration of the binding site. The results of this study were used in the SAMPL9 blinded binding affinity-prediction challenge, which are detailed in accompanying papers of the present Themed Collection. These data also open the phenothiazines and, potentially, chemically similar drugs, such as the tricyclic antidepressants, as relatively potent binders of ßCD, setting the stage for future SAMPL challenge datasets and for possible applications as drug reversal agents.

Tuning Potential Functions to Host-Guest Binding Data.

Software to more rapidly and accurately predict protein-ligand binding affinities is of high interest for early-stage drug discovery, and physics-based methods are among the most widely used technologies for this purpose. The accuracy of these methods depends critically on the accuracy of the potential functions that they use. Potential functions are typically trained against a combination of quantum chemical and experimental data. However, although binding affinities are among the most important quantities to predict, experimental binding affinities have not to date been integrated into the experimental data set used to train potential functions. In recent years, the use of host-guest complexes as simple and tractable models of binding thermodynamics has gained popularity due to their small size and simplicity, relative to protein-ligand systems. Host-guest complexes can also avoid ambiguities that arise in protein-ligand systems such as uncertain protonation states. Thus, experimental host-guest binding data are an appealing additional data type to integrate into the experimental data set used to optimize potential functions. Here, we report the extension of the Open Force Field Evaluator framework to enable the systematic calculation of host-guest binding free energies and their gradients with respect to force field parameters, coupled with the curation of 126 host-guest complexes with available experimental binding free energies. As an initial application of this novel infrastructure, we optimized generalized Born (GB) cavity radii for the OBC2 GB implicit solvent model against experimental data for 36 host-guest systems. This refitting led to a dramatic improvement in accuracy for both the training set and a separate test set with 90 additional host-guest systems. The optimized radii also showed encouraging transferability from host-guest systems to 59 protein-ligand systems. However, the new radii are significantly smaller than the baseline radii and lead to excessively favorable hydration free energies (HFEs). Thus, users of the OBC2 GB model currently may choose between GB cavity radii that yield more accurate binding affinities and GB cavity radii that yield more accurate HFEs. We suspect that achieving good accuracy on both will require more far-reaching adjustments to the GB model. We note that binding free-energy calculations using the OBC2 model in OpenMM gain about a 10× speedup relative to corresponding explicit solvent calculations, suggesting a future role for implicit solvent absolute binding free-energy (ABFE) calculations in virtual compound screening. This study proves the principle of using host-guest systems to train potential functions that are transferrable to protein-ligand systems and provides an infrastructure that enables a range of applications.

Target-Free Compound Activity Prediction via Few-Shot Learning.

Predicting the activities of compounds against protein-based or phenotypic assays using only a few known compounds and their activities is a common task in target-free drug discovery. Existing few-shot learning approaches are limited to predicting binary labels (active/inactive). However, in real-world drug discovery, degrees of compound activity are highly relevant. We study Few-Shot Compound Activity Prediction (FS-CAP) and design a novel neural architecture to meta-learn continuous compound activities across large bioactivity datasets. Our model aggregates encodings generated from the known compounds and their activities to capture assay information. We also introduce a separate encoder for the unknown compound. We show that FS-CAP surpasses traditional similarity-based techniques as well as other state of the art few-shot learning methods on a variety of target-free drug discovery settings and datasets.

The temperature-dependence of host-guest binding thermodynamics: experimental and simulation studies.

The thermodynamic parameters of host-guest binding can be used to describe, understand, and predict molecular recognition events in aqueous systems. However, interpreting binding thermodynamics remains challenging, even for these relatively simple molecules, as they are determined by both direct and solvent-mediated host-guest interactions. In this contribution, we focus on the contributions of water to binding by studying binding thermodynamics, both experimentally and computationally, for a series of nearly rigid, electrically neutral host-guest systems and report the temperature-dependent thermodynamic binding contributions ΔGb(T), ΔHb(T), ΔSb(T), and ΔCp,b. Combining isothermal titration calorimetry (ITC) measurements with molecular dynamics (MD) simulations, we provide insight into the binding forces at play for the macrocyclic hosts cucurbit[n]uril (CBn, n = 7-8) and ß-cyclodextrin (ß-CD) with a range of guest molecules. We find consistently negative changes in heat capacity on binding (ΔCp,b) for all systems studied herein - as well as for literature host-guest systems - indicating increased enthalpic driving forces for binding at higher temperatures. We ascribe these trends to solvation effects, as the solvent properties of water deteriorate as temperature rises. Unlike the entropic and enthalpic contributions to binding, with their differing signs and magnitudes for the classical and non-classical hydrophobic effect, heat capacity changes appear to be a unifying and more general feature of host-guest complex formation in water. This work has implications for understanding protein-ligand interactions and other complex systems in aqueous environments.

Structure-Activity Relationships, Tolerability and Efficacy of Microtubule-Active 1,2,4-Triazolo[1,5-a]pyrimidines as Potential Candidates to Treat Human African Trypanosomiasis.

Tubulin and microtubules (MTs) are potential protein targets to treat parasitic infections and our previous studies have shown that the triazolopyrimidine (TPD) class of MT-active compounds hold promise as antitrypanosomal agents. MT-targeting TPDs include structurally related but functionally diverse congeners that interact with mammalian tubulin at either one or two distinct interfacial binding sites; namely, the seventh and vinca sites, which are found within or between α,ß-tubulin heterodimers, respectively. Evaluation of the activity of 123 TPD congeners against cultured Trypanosoma brucei enabled a robust quantitative structure-activity relationship (QSAR) model and the prioritization of two congeners for in vivo pharmacokinetics (PK), tolerability and efficacy studies. Treatment of T. brucei-infected mice with tolerable doses of TPDs significantly decreased blood parasitemia within 24 h. Further, two once-weekly doses at 10 mg/kg of a candidate TPD significantly extended the survival of infected mice relative to infected animals treated with vehicle. Further optimization of dosing and/or the dosing schedule of these CNS-active TPDs may provide alternative treatments for human African trypanosomiasis.

Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field.

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

Alcohol and marijuana use predicting next-day absenteeism and engagement at school and work: A daily study of young adults.

This study examined effects of alcohol and marijuana use on next-day absenteeism and engagement at work and school among young adults (18-25 years old) who reported past-month alcohol use and simultaneous alcohol and marijuana use. Participants completed twice daily surveys for five, 14-day bursts. The analytic sample was 409 [64 % were enrolled in university (N = 263) and 95 % were employed (N = 387) in at least one burst]. Daily measures included: any alcohol or marijuana use, quantity of alcohol or marijuana use (i.e., number of drinks, number of hours high), attendance at work or school, and engagement (i.e., attentiveness, productivity) at school or work. Multilevel models examined between- and within-person associations between alcohol and marijuana use and next-day absenteeism and engagement at school or work. Between-persons, the proportion of days of alcohol use days was positively associated with next-day absence from school, consuming more drinks was positively associated with next-day absence from work, and the proportion of days of marijuana use was positively associated with next-day engagement at work. At the daily-level, when individuals consumed any alcohol and when they consumed more drinks than average, they reported lower next-day engagement during school and work. When individuals used marijuana and when they were high for more hours than average, they reported lower next-day engagement during school. Findings suggest alcohol and marijuana use consequences include next-day absence and decrements in next-day engagement at school and work, which could be included in interventions aimed at ameliorating harmful impacts of substance use among young adults.

Structure-Activity Relationships, Tolerability and Efficacy of Microtubule-Active 1,2,4-Triazolo[1,5- a ]pyrimidines as Potential Candidates to Treat Human African Trypanosomiasis.

Tubulin and microtubules (MTs) are potential protein targets to treat parasitic infections and our previous studies have shown that the triazolopyrimidine (TPD) class of MT- active compounds hold promise as antitrypanosomal agents. MT-targeting TPDs include structurally related but functionally diverse congeners that interact with mammalian tubulin at either one or two distinct interfacial binding sites; namely, the seventh and vinca sites, which are found within or between α,ß-tubulin heterodimers, respectively. Evaluation of the activity of 123 TPD congeners against cultured Trypanosoma brucei enabled a robust quantitative structure-activity relationship (QSAR) model and the prioritization of two congeners for in vivo pharmacokinetics (PK), tolerability and efficacy studies. Treatment of T. brucei -infected mice with tolerable doses of TPDs 3 and 4 significantly decreased blood parasitemia within 24 h. Further, two once-weekly doses of 4 at 10 mg/kg significantly extended the survival of infected mice relative to infected animals treated with vehicle. Further optimization of dosing and/or the dosing schedule of these CNS-active TPDs may provide alternative treatments for human African trypanosomiasis.

Development of Potent and Highly Selective Epoxyketone-Based Plasmodium Proteasome Inhibitors.

Here, we present remarkable epoxyketone-based proteasome inhibitors with low nanomolar in vitro potency for blood-stage Plasmodium falciparum and low cytotoxicity for human cells. Our best compound has more than 2,000-fold greater selectivity for erythrocytic-stage P. falciparum over HepG2 and H460 cells, which is largely driven by the accommodation of the parasite proteasome for a D-amino acid in the P3 position and the preference for a difluorobenzyl group in the P1 position. We isolated the proteasome from P. falciparum cell extracts and determined that the best compound is 171-fold more potent at inhibiting the ß5 subunit of P. falciparum proteasome when compared to the same subunit of the human constitutive proteasome. These compounds also significantly reduce parasitemia in a P. berghei mouse infection model and prolong survival of animals by an average of 6 days. The current epoxyketone inhibitors are ideal starting compounds for orally bioavailable anti-malarial drugs.

Substance-Specific Risk Factors for Cannabis and Alcohol Use Among Young Adults Following Implementation of Nonmedical Cannabis Legalization.

Laws regarding cannabis are rapidly changing in the USA as more states legalize nonmedical cannabis for adults aged 21 and older. Previous research has examined whether legalization has led to an increase in cannabis use as well as the use of other substances. The current study examined changes in cannabis- and alcohol-specific risk factors following legalization of nonmedical cannabis. We used 6 years of annual cross-sectional data (2014-2019) from 12,951 young adults age 18 to 25 who resided in Washington state. Risk factors examined include perceiving that use was common among same-age peers, believing use was acceptable, having easy access, and low perceived physical and psychological harm from use. Logistic regression models estimated annual rate of increase in these risk factors. All cannabis-specific risk factors increased among those aged 21+ (range of ORs for annual rate of change: 1.07-1.31) while significant increase in cannabis-related risk factors among those under age 21 was limited to perceptions of cannabis use being common (medical use: OR=1.08, 95% CI: 1.03, 1.12; nonmedical use: OR=1.13, 95% CI: 1.08, 1.18) and low perceived physical harm of occasional use (OR=1.08, 95% CI: 1.03, 1.13). Although descriptive norms for past-year use of alcohol among those aged 21+ increased (OR = 1.09, 95% CI: 1.02, 1.17), other risk factors for alcohol did not change significantly or, in the case of low perceived physical and psychological harm, decreased among both those under age 21 and those aged 21+ (range of ORs = 0.90-0.94). Given these findings show an increase in cannabis-specific risk factors since legalization was implemented, particularly among those young adults aged 21+, preventive interventions correcting risk misperceptions and related risk factors among young adults aged 21+ may prove efficacious in reducing use and resultant negative consequences.

Prom, graduation and parties: Alcohol use and normative perceptions among high school seniors during specific events.

BACKGROUND: Previous research has shown that a majority of adolescents in the United States initiate and drink alcohol prior to graduating high school and nearly twenty percent of high school seniors engage in heavy episodic drinking. Despite anecdotal evidence and media portrayals of alcohol use during high school events (e.g., prom), little is understood about alcohol use surrounding specific events that may be identified as "high-risk" events and addressed in specific interventions. Similarly, normative perceptions exert powerful influence on behaviors but little is understood about normative perceptions of alcohol use at high school events. OBJECTIVES: The purpose of the present study was to understand whether high school alcohol use is associated with specific events by describing behaviors and normative perceptions. METHODS: Participants were 386 U.S. college students age 18 to 19 (60.4 % female, mean age = 18.4) who provided retrospective accounts of their alcohol use surrounding senior year high school events (either before, which is relevant to pregaming addressed in this special issue, during, or after). RESULTS: Most students did not drink surrounding high school events but nearly all reported that they perceived that the typical high school senior did. Those who did drink alcohol tended to drink heavily, particularly during prom. Alcohol use was associated with other high school events ranging from the beginning of senior year (e.g., Homecoming) though the end (e.g., graduation parties) CONCLUSIONS: Results indicate the importance of future research efforts tailoring intervention efforts around specific events and the applicability of personalized normative feedback approaches.

LIMO: Latent Inceptionism for Targeted Molecule Generation.

Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physics-based methods. We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties. Comprehensive experiments show that LIMO performs competitively on benchmark tasks and markedly outperforms state-of-the-art techniques on the novel task of generating drug-like compounds with high binding affinity, reaching nanomolar range against two protein targets. We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted K D (a measure of binding affinity) of 6 · 10-14 M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets. Code is available at https://github.com/Rose-STL-Lab/LIMO.

Absolute binding free energy calculations improve enrichment of actives in virtual compound screening.

We determined the effectiveness of absolute binding free energy (ABFE) calculations to refine the selection of active compounds in virtual compound screening, a setting where the more commonly used relative binding free energy approach is not readily applicable. To do this, we conducted baseline docking calculations of structurally diverse compounds in the DUD-E database for three targets, BACE1, CDK2 and thrombin, followed by ABFE calculations for compounds with high docking scores. The docking calculations alone achieved solid enrichment of active compounds over decoys. Encouragingly, the ABFE calculations then improved on this baseline. Analysis of the results emphasizes the importance of establishing high quality ligand poses as starting points for ABFE calculations, a nontrivial goal when processing a library of diverse compounds without informative co-crystal structures. Overall, our results suggest that ABFE calculations can play a valuable role in the drug discovery process.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios. Predicted compounds will be tested rigorously in an experimental hub, and all predicted binders as well as all experimental screening data, including the chemical structures of experimentally tested compounds, will be made publicly available, and not subject to any intellectual property restrictions. The ability of a range of computational approaches to find novel binders will be evaluated, compared, and openly published. CACHE will launch 3 new benchmarking exercises every year. The outcomes will be better prediction methods, new small molecule binders for target proteins of importance for fundamental biology or drug discovery, and a major technological step towards achieving the goal of Target 2035, a global initiative to identify pharmacological probes for all human proteins.

"On a night like this": A mixed-methods approach to understanding high-risk drinking events in college students.

BACKGROUND: Previous research indicates college students report heavier drinking on certain events (e.g., 21st birthday). While past research has identified heavier drinking events, students' own reports of which events are associated with elevated drinking remains understudied. The current study utilized mixed methods to explore potential high-risk drinking events (HRDE) for college student drinkers and how these events differed from typical drinking and each other. METHODS: College student drinkers (N = 204) reported the number of drinks they consume on nine predetermined events (e.g., Halloween). Students also responded to open-ended questions listing five events during which they had elevated drinking and indicating the amount consumed on each event. Open-ended responses were coded into similar event categories. Descriptive statistics for drinks consumed were calculated for predetermined and coded open-ended events. Chi-square analyses assessed differences in endorsement of open-ended events by birth sex, age group, and Greek membership. Two multilevel count regressions assessed within-person differences in number of drinks consumed between participants' typical drinking occasions and (1) highly endorsed open-ended events and (2) predetermined events. RESULTS: For all open-ended event categories, average number of drinks consumed exceeded heavy episodic drinking thresholds; however, there was substantial variability. Comparing predetermined events to participants' typical drinking indicated elevated drinking on participants' birthdays, New Year's Eve, Halloween, Finals, and Spring Break; significant differences between events also emerged. Comparison of open-ended categories to participants' typical drinking indicated elevated drinking on birthdays, celebrations, parties, and holidays; however, there were no significant differences between open-ended events. CONCLUSIONS: Students who drink alcohol report heavier drinking on specific calendar-based events (e.g., Spring Break). However, students also report non-calendar-related events (e.g., non-specific parties) as some of their highest drinking events. More research is needed to understand how intervention and prevention programs can be adapted to target both known calendar-based HRDE, and unknown, idiosyncratic HRDE.

Alcohol and marijuana use, consequences, and perceived descriptive norms: Differences between two- and four-year college students.

Objective: Among two-year college students, alcohol and marijuana use, related consequences, and risk factors for use are not well understood. We examined differences between two- and four-year students in alcohol and marijuana use, consequences, and perceived descriptive norms, and explored whether two-year status moderated associations between norms and use. Participants: Data were drawn from a cross-sectional subsample of two- and four-year students aged 18-23 (n = 517) participating in a longitudinal study on alcohol use. Results: Four-year students reported greater alcohol use and consequences than two-year students; two-year students reported greater marijuana use than four-year students. Perceived alcohol and marijuana norms were positively related with use; two-year status did not moderate these associations. Conclusions: Perceived alcohol and marijuana norms function similarly for two- and four-year students in terms of associations to actual use. Adapting normative interventions for two-year students may be an effective strategy for reducing high-risk use among this underserved population.