LogP Model Research Articles

Do machines dream of atoms? Crippen's logP as a quantitative molecular benchmark for explainable AI heatmaps

While there is a great deal of interest in methods aimed at explaining machine learning predictions of chemical properties, it is difficult to quantitatively benchmark such methods, especially for regression tasks. We show that the Crippen logP model (J. Chem. Inf. Comput. Sci. 1999, 39, 868) provides an excellent benchmark for atomic attribution/heatmap approaches, especially if the ground truth heatmaps can be adjusted to reflect the molecular representation. The “atom attribution from finger prints”-method developed by Riniker and Landrum (J. Chem. Inf. Comput. Sci. 2013, 5, 43) gives atomic attribution heatmaps that are in reasonable agreement with the atomic contribution heatmaps of the Crippen logP model for most molecules, with average heatmap overlaps of up to 0.54. The agreement is increased significantly (to 0.75) when the atomic contributions are adjusted to match the fact that the molecular representation is fragment-based rather than atom-based (the finger print-adapted (FPA) ground truth vector). Most heatmaps and the corresponding FPA overlaps are relatively insensitive to the training set size and the results are close to converged for a training set size of 1000 molecules, although for molecules with low overlap some heatmaps change significantly. Using the “remove an atom” approach for graph convolutional neural networks (GCNNs) suggested by Matveieva and Polishchuk (J. Cheminform. 2021, 13, 41) we find an average heatmap overlap of 0.47 for the atomic contribution heatmaps of the Crippen logP model. Like the simpler attribution benchmarks for classification tasks that have come before it, this work sets the bar for regression tasks.

Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol-water partition coefficient (logP) for druglike molecules using MM-PBSA method.

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient(R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.

Experimental study on consolidation characteristics of deep clayey soil in a typical subsidence area of the North China Plain

Land subsidence is an important environmental problem in the North China Plain (NCP). A typical subsidence area mainly located on adjoining areas of Beijing and Hebei provinces was selected to study the consolidation characteristics of deep clayey soil. Clay samples were collected from 14 boreholes at different depths, and the compression and consolidation behavior of the soil was analyzed by high-pressure oedometer tests. Results show that the deformation amount and deformation stabilization time in the primary consolidation stage increase with the load but decrease with the sampling depth. The nonlinear compression model of e-log(p) does not fit well for deep normally-consolidated clayey soil, the log(e+ec)–logp model fit the shallow clayey soil better than the deep clayey soil, and the hyperbolic model fits almost all soil samples well. Based on statistical theory, the soil consolidation equation CE_NCP was established for calculating the relationship between the degree of consolidation and the time factor in subsidence areas. The equations were fitted with the data obtained from high-pressure consolidation tests to obtain three parameters a, b and c, which are applied to the consolidation calculations for soil samples at depths of 0–100 m, 100–200 m, 200–300 m and 300–400 m, respectively. The applicability of CE_NCP equation was evaluated by comparing it to the analytical solution of Terzaghi’s consolidation equation, results showing that CE_NCP equation is more suitable for the calculation of the consolidation in the study area than the Terzaghi’s analytical solution. Eventually, the CE_NCP equation was successfully used to estimate the degree of consolidation in a subsidence area of Lang Fang City in the NCP.

Adaptive MPI collective operations based on evaluations in LogP model

Message passing model, represented by MPI (Message Passing Interface), is the principal parallel programming tool for distributed computer systems. The most of MPI-programs contain collective communications, which involve all the processes of a parallel program. Effectiveness of collective communications substantially effects on total time of program execution. In this work, we consider the problem of design of adaptive algorithms of collective communications on the example of barrier synchronization, which refers to one of the most common types of collective communications. We developed adaptive algorithm of barrier synchronization, which suboptimally selects barrier synchronization scheme in parallel MPI-programs among such algorithms as Central Counter, Combining Tree and Dissemination Barrier. The adaptive algorithm chooses the barrier algorithm with the minimal evaluation of execution time in the model LogP. Model LogP considers performance of computational resources and interconnect for point-to-point communications. Proposed algorithm has been implemented for MPI. We present the results of experiments on cluster systems, analyse dependency of algorithm selection on LogP parameters values. In particular, for the number of processes less than 20 adaptive algorithm selects Combining Tree, while for a larger number of processes adaptive algorithm selects Dissemination Barrier. Developed algorithm minimizes average time of barrier synchronization by 4%, in comparison with the most common determined barrier algorithms.

Retention behavior of flavonoids on immobilized artificial membrane chromatography and correlation with cell-based permeability.

The aim of the study was to investigate the immobilized artificial membrane (IAM) retention mechanism for a set of flavonoids and to evaluate the potential of IAM chromatography to model Caco-2 permeability. For this purpose, the retention behavior of 41 flavonoid analogs on two IAM stationary phases, IAM.PC.MG and IAM.PC.DD2, was investigated. Correlations between retention factors, logkw(IAM) and octanol-water partitioning (logP) were established and the role of hydroxyl groups of flavonoids to the underlying retention mechanism was explored. IAM retention and logP values were used to establish sound linear models with Caco-2 permeability (logPapp ) taken from the literature. Both stepwise regression and multivariate analysis confirmed the contribution of hydrogen bond descriptors, as additional parameters in the either logkw(IAM) or logP models. Retention factors on both IAM stationary phases showed comparable performance with n-octanol-water partitioning towards Caco-2 permeability.

The SAMPL5 challenge for embedded-cluster integral equation theory: solvation free energies, aqueous pK a, and cyclohexane-water log D.

We predict cyclohexane-water distribution coefficients (logD 7.4) for drug-like molecules taken from the SAMPL5 blind prediction challenge by the "embedded cluster reference interaction site model" (EC-RISM) integral equation theory. This task involves the coupled problem of predicting both partition coefficients (logP) of neutral species between the solvents and aqueous acidity constants (pK a) in order to account for a change of protonation states. The first issue is addressed by calibrating an EC-RISM-based model for solvation free energies derived from the "Minnesota Solvation Database" (MNSOL) for both water and cyclohexane utilizing a correction based on the partial molar volume, yielding a root mean square error (RMSE) of 2.4kcalmol-1 for water and 0.8-0.9kcalmol-1 for cyclohexane depending on the parametrization. The second one is treated by employing on one hand an empirical pK a model (MoKa) and, on the other hand, an EC-RISM-derived regression of published acidity constants (RMSE of 1.5 for a single model covering acids and bases). In total, at most 8 adjustable parameters are necessary (2-3 for each solvent and two for the pK a) for training solvation and acidity models. Applying the final models to the logD 7.4 dataset corresponds to evaluating an independent test set comprising other, composite observables, yielding, for different cyclohexane parametrizations, 2.0-2.1 for the RMSE with the first and 2.2-2.8 with the combined first and second SAMPL5 data set batches. Notably, a pure logP model (assuming neutral species only) performs statistically similarly for these particular compounds. The nature of the approximations and possible perspectives for future developments are discussed.

Global Model for Octanol-Water Partition Coefficients from Proton Nuclear Magnetic Resonance Spectra.

The ability to estimate chemical and physical properties from experimental spectra is highly desirable, as it eliminates the need for a priori knowledge of exact chemical structure and allows the property estimation of mixtures. Here we report the proof of principle that a predictive method for octanol-water partition coefficient (logP) based on (1) H-NMR spectra in d(3) -chloroform is feasible and can yield accuracy comparable to in silico logP models. The Spectrometric Data-Activity Relationship (QSDAR) reported predicts logP of neutral organic chemicals using descriptors derived only from (1) H-NMR chemical shifts, integrations and peak widths. Proton NMR spectra of 140 compounds with diverse structures were used to construct a Multiple Linear Regression (MLR) and a Partial Least Squares (PLS) model that predicts logP. The optimized models were internally validated by K-fold cross validation and leave-one-out validation, and externally with a test set of 28 chemicals. The squared regression coefficients of prediction for the MLR and PLS regression models were 0.970 and 0.971 respectively, showing that the method allows accurate prediction of logP values exclusively from predicted (1) H NMR spectra.

LogGPO: An accurate communication model for performance prediction of MPI programs

Message passing interface (MPI) is the de facto standard in writing parallel scientific applications on distributed memory systems. Performance prediction of MPI programs on current or future parallel systems can help to find system bottleneck or optimize programs. To effectively analyze and predict performance of a large and complex MPI program, an efficient and accurate communication model is highly needed. A series of communication models have been proposed, such as the LogP model family, which assume that the sending overhead, message transmission, and receiving overhead of a communication is not overlapped and there is a maximum overlap degree between computation and communication. However, this assumption does not always hold for MPI programs because either sending or receiving overhead introduced by MPI implementations can decrease potential overlap for large messages. In this paper, we present a new communication model, named LogGPO, which captures the potential overlap between computation with communication of MPI programs. We design and implement a trace-driven simulator to verify the LogGPO model by predicting performance of point-to-point communication and two real applications CG and Sweep3D. The average prediction errors of LogGPO model are 2.4% and 2.0% for these two applications respectively, while the average prediction errors of LogGP model are 38.3% and 9.1% respectively.

Techniques for pipelined broadcast on ethernet switched clusters

By splitting a large broadcast message into segments and broadcasting the segments in a pipelined fashion, pipelined broadcast can achieve high performance in many systems. In this paper, we investigate techniques for efficient pipelined broadcast on clusters connected by multiple Ethernet switches. Specifically, we develop algorithms for computing various contention-free broadcast trees that are suitable for pipelined broadcast on Ethernet switched clusters, extend the parametrized LogP model for predicting appropriate segment sizes for pipelined broadcast, show that the segment sizes computed based on the model yield high performance, and evaluate various pipelined broadcast schemes through experimentation on Ethernet switched clusters with various topologies. The results demonstrate that our techniques are practical and efficient for contemporary fast Ethernet and Giga-bit Ethernet clusters.

Efficient algorithms for parallelizing Monte Carlo simulations for 2D Ising spin models

In this paper, we design and implement a variety of parallel algorithms for both sweep spin selection and random spin selection. We analyze our parallel algorithms on LogP, a portable and general parallel machine model. We then obtain rigorous theoretical runtime results on LogP for all the parallel algorithms. Moreover, a guiding equation is derived for choosing data layouts (blocked vs. stripped) for sweep spin selection. In regard to random spin selection, we are able to develop parallel algorithms with efficient communication schemes. We introduce two novel schemes, namely the FML scheme and the ?-scheme. We analyze randomness of our schemes using statistical methods and provide comparisons between the different schemes.

SVM approach for predicting LogP

The logarithm of the partition coefficient between n-octanol and water (logP) is an important parameter for drug discovery. Based upon the comparison of several prediction logP models, i.e. Support Vector Machines (SVM), Partial Least Squares (PLS) and Multiple Linear Regression (MLR), the authors reported SVM model is the best one in this paper.

On stalling in LogP

We investigate the issue of stalling in the LogP model. In particular, we introduce a novel quantitative characterization of stalling, referred to as δ - stalling, which intuitively captures the realistic assumption that once the network's capacity constraint is violated, it takes some time (at most δ ) for this information to propagate to the processors involved. We prove a lower bound that shows that LogP under δ -stalling is strictly more powerful than the stall-free version of the model where only strictly stall-free computations are permitted. On the other hand, we show that δ -stalling LogP with δ = L can be simulated with at most logarithmic slowdown by a BSP machine with similar bandwidth and latency values, thus extending the equivalence (up to logarithmic factors) between stall-free LogP and BSP argued in Bilardi et al. (Algorithmica 24 (1999) 405) and Ramachandran et al. (J. Parallel Distributed Comput. 63 (2003) 1175) to the more powerful L -stalling LogP.

Emulations between QSM, BSP and LogP: a framework for general-purpose parallel algorithm design

We present work-preserving emulations with small slowdown between LogP and two other parallel models: BSP and QSM. In conjunction with earlier work-preserving emulations between QSM and BSP, these results establish a close correspondence between these three general-purpose parallel models. Our results also correct and improve on results reported earlier on emulations between BSP and LogP. In particular we shed new light on the relative power of stalling and non-stalling LogP models. The QSM is a shared-memory model with only two parameters— p, the number of processors, and g, a bandwidth parameter. The simplicity of the QSM parameters makes QSM a convenient model for parallel algorithm design, and simple work-preserving emulations of QSM on BSP and QSM on LogP show that algorithms designed for the QSM will also map quite well to these other models. The simplicity and generality of QSM present a strong case for the use of QSM as the model of choice for parallel algorithm design. We present QSM algorithms for three basic problems—prefix sums, sample sort and list ranking. We show that these algorithms are optimal in terms of both the total work performed and the number of ‘phases’ for input sizes of practical interest. For prefix sums, we present a matching lower bound that shows our algorithm to be optimal over the complete range of these parameters. We then examine the predicted and simulated performance of these algorithms. These results suggest that QSM analysis will predict algorithm performance quite accurately for problem sizes that arise in practice.

Latency, occupancy, and bandwidth in dsm multiprocessors: a performance evaluation

While the desire to use commodity parts in the communication architecture of a DSM multiprocessor offers advantages in cost and design time, the impact on application performance is unclear. We study this performance impact through detailed simulation, analytical modeling, and experiments on a flexible DSM prototype, using a range of parallel applications. We adapt the logP model to characterize the communication architectures of DSM machines. The l (network latency) and o (controller occupancy) parameters are the keys to performance in these machines, with the g (node-to-network bandwidth) parameter becoming important only for the fastest controllers. We show that, of all the logP parameters, controller occupancy has the greatest impact on application performance. Of the two contributions of occupancy to performance degradation-the latency it adds and the contention it induces-it is the contention component that governs performance regardless of network latency, showing a quadratic dependence on o. As expected, techniques to reduce the impact of latency make controller occupancy a greater bottleneck. Surprisingly, the performance impact of occupancy is substantial, even for highly-tuned applications and even in the absence of latency hiding techniques. Scaling the problem size is often used as a technique to overcome limitations in communication latency and bandwidth. Through experiments on a DSM prototype, we show that there are important classes of applications for which the performance lost by using higher occupancy controllers cannot be regained easily, if at all, by scaling the problem size.

Dynamic TCP acknowledgment in the LogP model

When messages, which are to be sent point-to-point in a network, become available at irregular intervals, a decision must be made each time a new message becomes available as to whether it should be sent immediately or if it is better to wait for more messages and send them all together. Because of physical properties of the networks, a certain minimum amount of time must elapse in between the transmission of two packets. Thus, whereas waiting delays the transmission of the current data, sending immediately may delay the transmission of the next data to become available even more. We propose a new quality measure and derive optimal deterministic and randomized algorithms for this on-line problem.

Parallel Complexity of Matrix Multiplication

Effective design of parallel matrix multiplication algorithms relies on the consideration of many interdependent issues based on the underlying parallel machine or network upon which such algorithms will be implemented, as well as, the type of methodology utilized by an algorithm. In this paper, we determine the parallel complexity of multiplying two (not necessarily square) matrices on parallel distributed-memory machines and/or networks. In other words, we provided an achievable parallel run-time that can not be beaten by any algorithm (known or unknown) for solving this problem. In addition, any algorithm that claims to be optimal must attain this run-time. In order to obtain results that are general and useful throughout a span of machines, we base our results on the well-known LogP model. Furthermore, three important criteria must be considered in order to determine the running time of a parallel algorithm; namely, (i) local computational tasks, (ii) the initial data layout, and (iii) the communication schedule. We provide optimality results by first proving general lower bounds on parallel run-time. These lower bounds lead to significant insights on (i)–(iii) above. In particular, we present what types of data layouts and communication schedules are needed in order to obtain optimal run-times. We prove that no one data layout can achieve optimal running times for all cases. Instead, optimal layouts depend on the dimensions of each matrix, and on the number of processors. Lastly, optimal algorithms are provided.

Efficient and Optimal Parallel Algorithms for Cholesky Decomposition

In this dissertation we consider design issues such as data, layout, data, dependency, communication scheduling, and parallel models in order to develop parallel algorithms for Cholesky decomposition. Our intention is to design efficient and portable parallel algorithms for large scale dense linear systems to be run in the distributed memory environment. To consider the issue of portability, we utilize the well known LogP model for designing parallel algorithms and for performing theoretical run time analysis. We developed a contention free communication scheduling for the parallel algorithms in order to reduce the communication cost. To study the impact of data layout on performance, we designed the parallel algorithms based on column, row and block data distribution utilizing the fan-in and fan-out methods. Specifically we introduced panel size for 1D layouts and granularity size for 2D layouts for the task partitioning. To determine the efficiency of the parallel algorithms, we derived lower bound results and theoretical run time performance. The analysis on the parallel algorithms shows that parallel algorithms for 1D data layouts are optimal. However for 2D data layouts, the parallel algorithms are efficient, if not necessarily asymptotically optimal. We then implemented the parallel algorithms using MPI. The experimental results show that fan-in outperforms fan-out for 2D layouts, especially for larger processor size. The performance for the 1D fan-out row-based parallel algorithm was no worse than that of the column-based parallel algorithms. This seems to refute the common notion that row-based parallel algorithms are not, as efficient as column-based parallel algorithms. The experimental results indicated that performance was impacted as panel size and/or granularity size varied. This provides insights that the constant factors for lower bound and run time performance call not be ignored.

General and class specific models for prediction of soil sorption using various physicochemical descriptors.

Diverse chemical descriptors were explored for use in QSAR models aimed to screen the soil sorption potential of organic compounds. The descriptors included logP, HyperChem QSARProperties descriptors, a combination of connectivity indices, geometrical, and quantum chemical measures, and two sets from the DRAGON and CODESSA program packages, respectively. Generally, the univariate logP models were capable of capturing most of the variation and give an indication of the sorption potential. The multivariate models required refined variable selection procedures but were shown to include crucial descriptors for modeling compound classes with specific chemical characteristics.

Optimal and Efficient Algorithms for Summing and Prefix Summing on Parallel Machines

The problem of designing efficient parallel algorithms for summing and prefix summing for certain classes of the LogP model is studied. We present optimal algorithms for summing and show that any optimal summing algorithm must have a certain inherent structure. Moreover, we present optimal or near-optimal algorithms for prefix summing for both non-commutative and commutative binary operators. Furthermore, we show that the optimal algorithms for prefix summing for these two types of operators are not equivalent.

PERFORMANCE EVALUATION OF PRACTICAL PARALLEL COMPUTER MODEL LogPQ

The present super computer will be replaced by a massively parallel computer consisting of a large number of processing elements which satisfy the continuous increasing depend for computing power. Practical parallel computing model has been expected to develop efficient parallel algorithms on massively parallel computers. Thus, we have presented a practical parallel computation model LogPQ by taking account of communication queues into the LogP model. This paper addresses the performance of a parallel matrix multiplication algorithm using LogPQ and LogP models. The parallel algorithm is implemented on Cray T3E and the parallel performances are compared with on the old machine CM-5. This shows that the communication network of T3E has superior buffering behavior than CM-5, in which we don't need to prepare extra buffering on T3E. Although, a little effect remains for both of the send and receive bufferings. On the other hand, the effect of message size remains, which shows the necessity of the overhead and gap proportional to the message size.