7 research outputs found
Kernel Methods in Computer-Aided Constructive Drug Design
A drug is typically a small molecule that interacts with the binding site of some
target protein. Drug design involves the optimization of this interaction so that the
drug effectively binds with the target protein while not binding with other proteins
(an event that could produce dangerous side effects). Computational drug design
involves the geometric modeling of drug molecules, with the goal of generating
similar molecules that will be more effective drug candidates. It is necessary that
algorithms incorporate strategies to measure molecular similarity by comparing
molecular descriptors that may involve dozens to hundreds of attributes. We use
kernel-based methods to define these measures of similarity. Kernels are general
functions that can be used to formulate similarity comparisons.
The overall goal of this thesis is to develop effective and efficient computational
methods that are reliant on transparent mathematical descriptors of molecules with
applications to affinity prediction, detection of multiple binding modes, and generation
of new drug leads. While in this thesis we derive computational strategies for
the discovery of new drug leads, our approach differs from the traditional ligandbased
approach. We have developed novel procedures to calculate inverse mappings
and subsequently recover the structure of a potential drug lead. The contributions
of this thesis are the following:
1. We propose a vector space model molecular descriptor (VSMMD) based on
a vector space model that is suitable for kernel studies in QSAR modeling.
Our experiments have provided convincing comparative empirical evidence
that our descriptor formulation in conjunction with kernel based regression
algorithms can provide sufficient discrimination to predict various biological
activities of a molecule with reasonable accuracy.
2. We present a new component selection algorithm KACS (Kernel Alignment
Component Selection) based on kernel alignment for a QSAR study. Kernel
alignment has been developed as a measure of similarity between two kernel
functions. In our algorithm, we refine kernel alignment as an evaluation tool,
using recursive component elimination to eventually select the most important
components for classification. We have demonstrated empirically and proven
theoretically that our algorithm works well for finding the most important
components in different QSAR data sets.
3. We extend the VSMMD in conjunction with a kernel based clustering algorithm
to the prediction of multiple binding modes, a challenging area of
research that has been previously studied by means of time consuming docking
simulations. The results reported in this study provide strong empirical
evidence that our strategy has enough resolving power to distinguish multiple
binding modes through the use of a standard k-means algorithm.
4. We develop a set of reverse engineering strategies for QSAR modeling based
on our VSMMD. These strategies include:
(a) The use of a kernel feature space algorithm to design or modify descriptor
image points in a feature space.
(b) The deployment of a pre-image algorithm to map the newly defined
descriptor image points in the feature space back to the input space of
the descriptors.
(c) The design of a probabilistic strategy to convert new descriptors to meaningful
chemical graph templates.
The most important aspect of these contributions is the presentation of strategies that actually generate the structure of a new drug candidate. While the training
set is still used to generate a new image point in the feature space, the reverse engineering
strategies just described allows us to develop a new drug candidate that is
independent of issues related to probability distribution constraints placed on test
set molecules
Learning speech embeddings for speaker adaptation and speech understanding
In recent years, deep neural network models gained popularity as a modeling approach for many speech processing tasks including automatic speech recognition (ASR) and spoken language understanding (SLU). In this dissertation, there are two main goals. The first goal is to propose modeling approaches in order to learn speaker embeddings for speaker adaptation or to learn semantic speech embeddings. The second goal is to introduce training objectives that achieve fairness for the ASR and SLU problems. In the case of speaker adaptation, we introduce an auxiliary network to an ASR model and learn to simultaneously detect speaker changes and adapt to the speaker in an unsupervised way. We show that this joint model leads to lower error rates as compared to a two-step approach where the signal is segmented into single speaker regions and then fed into an adaptation model. We then reformulate the speaker adaptation problem from a counterfactual fairness point-of-view and introduce objective functions to match the ASR performance of the individuals in the dataset to that of their counterfactual counterparts. We show that we can achieve lower error rate in an ASR system while reducing the performance disparity between protected groups. In the second half of the dissertation, we focus on SLU and tackle two problems associated with SLU datasets. The first SLU problem is the lack of large speech corpora. To handle this issue, we propose to use available non-parallel text data so that we can leverage the information in text to guide learning of the speech embeddings. We show that this technique increases the intent classification accuracy as compared to a speech-only system. The second SLU problem is the label imbalance problem in the datasets, which is also related to fairness since a model trained on skewed data usually leads to biased results. To achieve fair SLU, we propose to maximize the F-measure instead of conventional cross-entropy minimization and show that it is possible to increase the number of classes with nonzero recall. In the last two chapters, we provide additional discussions on the impact of these projects from both technical and social perspectives, propose directions for future research and summarize the findings
Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting
Abstract — Recently, we proposed an improvement to the conventional eigenvoice (EV) speaker adaptation using kernel methods. In our novel kernel eigenvoice (KEV) speaker adaptation [1], speaker supervectors are mapped to a kernelinduced high dimensional feature space, where eigenvoices are computed using kernel principal component analysis. A new speaker model is then constructed as a linear combination of the leading eigenvoices in the kernel-induced feature space. KEV adaptation was shown to outperform EV, MAP, and MLLR adaptation in a TIDIGITS task with less than 10s of adaptation speech [2]. Nonetheless, due to many kernel evaluations, both adaptation and subsequent recognition in KEV adaptation are considerably slower than conventional EV adaptation. In this paper, we solve the efficiency problem and eliminate all kernel evaluations involving adaptation or testing observations by finding an approximate preimage of the implicit adapted model found by KEV adaptation in the feature space; we call our new method embedded kernel eigenvoice (eKEV) adaptation. eKEV adaptation is faster than KEV adaptation, and subsequent recognition runs as fast as normal HMM decoding. eKEV adaptation makes use of multi-dimensional scaling technique so that the resulting adapted model lies in the span of a subset of carefully chosen training speakers. It is related to the reference speaker weighting (RSW) adaptation method that is based on speaker clustering. Our experimental results on Wall Street Journal show that eKEV adaptation continues to outperform EV, MAP, MLLR, and the original RSW method. However, by adopting the way we choose the subset of reference speakers for eKEV adaptation, we may also improve RSW adaptation so that it performs as well as our eKEV adaptation