Andy Liaw

Merck & Co. | MSD

Inhibiting Arginase 1 (ARG1), a metalloenzyme that hydrolyzes L-arginine in the urea cycle, has been demonstrated as a promising therapeutic avenue in immuno-oncology through the restoration of suppressed immune response in several types of cancers. Most of the currently reported small molecule inhibitors are boronic acid based. Herein, we report t...

Quantification of subvisible particles, which are generally defined as those ranging in size from 2 to 100 µm, is important as critical characteristics for biopharmaceutical formulation development. Micro Flow Imaging (MFI) provides quantifiable morphological parameters to study both the size and type of subvisible particles, including proteinaceou...

The multidrug resistance protein 1 (MDR1) P-glycoprotein (P-gp) is a clinically important transporter. In vitro P-gp inhibition assays have been routinely conducted to predict the potential for clinical drug-drug interactions (DDIs) mediated by P-gp. However, high inter- laboratory and inter-system variability of P-gp IC50 data limits accurate pred...

High-throughput phenotypic screening is a key driver for the identification of novel chemical matter in drug discovery for challenging targets, especially for those with an unclear mechanism of pathology. For toxic or gain-of-function proteins, small-molecule suppressors are a targeting/therapeutic strategy that has been successfully applied. As wi...

In the pharmaceutical industry, where it is common to generate many QSAR models with large numbers of molecules and descriptors, the best QSAR methods are those that can generate the most accurate predictions but that are also insensitive to hyperparameters and are computationally efficient. Here we compare Light Gradient Boosting Machine (LightGBM...

While Gaussian process models are typically restricted to smaller data sets, we propose a variation which extends its applicability to the larger data sets common in the industrial drug discovery space, making it relatively novel in the quantitative structure-activity relationship (QSAR) field. By incorporating locality-sensitive hashing for fast n...

Mass spectrometry-based discovery proteomics is an essential tool for the proximal readout of cellular drug action. Here, we apply a robust proteomic workflow to rapidly profile the proteomes of five lung cancer cell lines in response to more than 50 drugs. Integration of millions of quantitative protein–drug associations substantially improved the...

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Protein redesign and engineering has become an important task in pharmaceutical research and development. Recent advances in technology have enabled efficient protein redesign by mimicking natural evolutionary mutation, selection and amplification steps in the laboratory environment. For any given protein, the number of possible mutations is astron...

Given a particular descriptor/method combination, we find some QSAR datasets are very predictive by random-split cross-validation, while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the limits are due to activity cliffs. Here we investigate, on in-h...

Interactions between transmembrane receptors and their ligands play important roles in normal biological processes and pathological conditions. However, the binding partners for many transmembrane-like proteins remain elusive. To identify potential ligands of these orphan receptors, we developed a screening platform using a homogenous nonwash bindi...

Quantitative structure-activity relationship (QSAR) is a very commonly used technique for predicting biological activity of a molecule using information contained in the molecular descriptors. The large number of compounds and descriptors and sparseness of descriptors pose important challenges to traditional statistical methods and machine learning...

Chemoproteomics is a powerful mass spectrometry?based affinity chromatography approach for identifying proteome-wide small molecule-protein interactions.1 It aims for unbiased determination of drug targets in a complex cellular environment. Chemoproteomics has been one of the central methods of choice for small molecule mechanism of action (MOA) de...

Deep neural networks (DNNs) are complex computational models that have found great success in many artificial intelligence applications, such as computer vision and natural language processing. In the past four years, DNNs also generated promising results for quantitative structure-activity relationship (QSAR) tasks. Previous work showed that DNNs...

Background: SREBP cleavage-activating protein (SCAP) is a cholesterol binding endoplasmic reticulum (ER) membrane protein that is required to activate SREBP transcription factors. SREBPs regulate genes involved in lipid biosynthesis. They also influence lipid clearance by modulating the expression of LDL receptor (LDLR) and proprotein convertase s...

In the pharmaceutical industry it is common to generate many QSAR models from training sets containing a large number of molecules and a large number of descriptors. The best QSAR methods are those that can generate the most accurate predictions but that are not overly expensive computationally. In this paper we compare extreme gradient boosting (X...

SREBP cleavage-activating protein (SCAP) is a key protein in the regulation of lipid metabolism and a potential target for treatment of dyslipidemia. SCAP is required for activation of the transcription factors SREBP-1 and -2. SREBPs regulate the expression of genes involved in fatty acid and cholesterol biosynthesis, and LDL-C clearance through th...

Disease modifying treatments for Alzheimer's disease (AD) constitute a major goal in medicine. Current trends suggest that biomarkers reflective of AD neuropathology and modifi-able by treatment would provide supportive evidence for disease modification. Nevertheless, a lack of quantitative tools to assess disease modifying treatment effects remain...

Inhibition of hepatic transporters such as organic anion transporting polypeptides (OATPs) 1B can cause drug-drug interactions (DDIs). Determining the impact of perpetrator drugs on the plasma exposure of endogenous substrates for OATP1B could be valuable to assess the risk for DDIs early in drug development. As OATP1B orthologs are well conserved...

Neural networks were widely used for Quantitative Structure-Activity Relationships (QSAR), in the 1990's. Because of various practical issues (e.g. slow on large problems, difficult to train, prone to over-fitting, etc.), they were superseded by more robust methods like Support Vector Machine (SVM) and Random Forest (RF), which arose in the early 2...

This chapter describes a very important application of statistical methods to drug discovery, namely quantitative structure-activity relationship (QSAR) models. These models are most often used in the lead optimization stage, when a few families of molecules have been identified as active in vitro against the biological target of interest. The chap...

Experimental methods in cell biology offer an excellent combination of access to detailed biologic information together with manageable cost. Recent technological advances have further enhanced this capacity, allowing the interrogation of cells in the automated high throughput mode that is necessary in the pharmaceutical industry. While the technol...

Background Early biomarkers of skeletal muscle anabolism will facilitate the development of therapies for sarcopenia and frailty. Methods and results We examined plasma type III collagen N-terminal propeptide (P3NP), skeletal muscle protein fractional synthesis rate, and gene and protein expression profiles to identify testosterone-induced changes...

Top-down mass spectrometry holds tremendous potential for the characterization and quantification of intact proteins, including individual protein isoforms and specific posttranslationally modified forms. This technique does not require antibody reagents and thus offers a rapid path for assay development with increased specificity based on the amin...

The rapid identification of protein biomarkers in biofluids is important to drug discovery and development. Here, we describe a general proteomic approach for the discovery and identification of proteins that exhibit a statistically significant difference in abundance in cerebrospinal fluid (CSF) before and after pharmacological intervention. This...

Proteomics was utilized to identify novel potential plasma biomarkers of exercise-induced muscle injury. Muscle injury was induced in nine human volunteers by eccentric upper extremity exercise. Liquid chromatography-mass spectrometry identified 30 peptides derived from nine proteins which showed significant change in abundance post-exercise. Four...

This paper proposes a new automatic hypothesis-generation algorithm for structure–activity relationship (SAR) rules, which is capable of investigating chemical compound activities in the context of multiple substructure interactions. The algorithm is formulated as an optimization problem based on a carefully selected criterion, APostDiff(s), and th...

Estrogens are a class of steroid hormones that interact with two related but distinct nuclear receptors, estrogen receptor (ER) alpha and beta. To identify potential ER biomarkers, we profiled the rat plasma glycoproteome after treatment with vehicle or 17beta-estradiol (E2) or an ERalpha-selective agonist PPT by differential mass spectrometry. Our...

The task of modeling the distribution of a large number of tree species under future climate scenarios presents unique challenges. First, the model must be robust enough to handle climate data outside the current range without producing unacceptable instability in the output. In addition, the technique should have automatic search mechanisms built...

A classification and regression tool, J. H. Friedman's Stochastic Gradient Boosting (SGB), is applied to predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Stochastic Gradient Boosting is a procedure for building a sequence of models, for instance regres...

Leo Breiman’s Random Forest ensemble learning procedure is applied to the problem of Quantitative Structure-Activity Relationship (QSAR) modeling for pharmaceutical molecules. This entails using a quantitative description of a compound’s molecular structure to predict that compound’s biological activity as measured in an in vitro assay. Without any...

A wrapper variable selection procedure is proposed for use with learning machines that generate a measure of variable importance, such as Random Forest. The procedure is based on iteratively removing low-ranking variables and assessing the learning machine performance by cross-validation. The procedure is implemented for Random Forest on some QSAR...

High-throughput screening (HTS) is used in modern drug discovery to screen hundreds of thousands to millions of compounds on selected protein targets. It is an industrial-scale process relying on sophisticated automation and state-of-the-art detection technologies. Quality control (QC) is an integral part of the process and is used to ensure good q...

High-throughput screening (HTS) plays a central role in modern drug discovery, allowing the rapid screening of large compound collections against a variety of putative drug targets. HTS is an industrial-scale process, relying on sophisticated automation, control, and state-of-the art detection technologies to organize, test, and measure hundreds of...

A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples...

The problem of identifying novel samples from a library of mass spectral data is seen as the problem of identifying outliers in very high dimensional space. We pro- pose a method of identifying such novel samples based on hierarchical clustering. The method produces a measure of outlyingness for each sample in the library, which can be used to choo...