We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using ...graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.
The 'inverse problem' of mass spectrometric molecular identification ('given a mass spectrum, calculate/predict the 2D structure of the molecule whence it came') is largely unsolved, and is ...especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ('calculate a small molecule's likely fragmentation and hence at least some of its mass spectrum from its structure alone') is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the 'translation' a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the 'true' molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are 'similar' to the top hit. In addition to using the 'top hits' directly, we can produce a rank order of these by 'round-tripping' candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower, including those in the last CASMI challenge (for which the results are known), getting 49/93 (53%) precisely correct. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. It seems to act as a Las Vegas algorithm, in that it either gives the correct answer or simply states that it cannot find one. The ability to create and to 'learn' millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.
Background:
A question that lies at the very heart of language acquisition research is how children learn semi-regular systems with exceptions (e.g., the English plural rule that yields
cats, dogs
, ...etc, with exceptions
feet
and
men
). We investigated this question for Hindi ergative
ne
marking; another semi-regular but exception-filled system. Generally, in the past tense, the subject of two-participant transitive verbs (e.g.,
Ram broke the cup
) is marked with
ne
, but there are exceptions. How, then, do children learn when
ne
marking is required, when it is optional, and when it is ungrammatical?
Methods:
We conducted two studies using (a) acceptability judgment and (b) elicited production methods with children (aged 4-5, 5-6 and 9-10 years) and adults.
Results:
All age groups showed effects of
statistical preemption
: the greater the frequency with which a particular verb appears with versus without
ne
marking on the subject – relative to other verbs – the greater the extent to which participants (a) accepted and (b) produced
ne
over zero-marked subjects. Both children and adults also showed effects of clause-level semantics, showing greater acceptance of
ne
over zero-marked subjects for intentional than unintentional actions. Some evidence of semantic effects at the level of the verb was observed in the elicited production task for children and the judgment task for adults.
Conclusions:
participants mainly learn ergative marking on an input-based verb-by-verb basis (i.e., via statistical preemption; verb-level semantics), but are also sensitive to clause-level semantic considerations (i.e., the intentionality of the action). These findings add to a growing body of work which suggests that children learn semi-regular, exception-filled systems using both statistics and semantics.
Background:
A question that lies at the very heart of language acquisition research is how children learn semi-regular systems with exceptions (e.g., the English plural rule that yields
cats, dogs
, ...etc, with exceptions
feet
and
men
). We investigated this question for Hindi ergative
ne
marking; another semi-regular but exception-filled system. Generally, in the past tense, the subject of two-participant transitive verbs (e.g.,
Ram broke the cup
) is marked with
ne
, but there are exceptions. How, then, do children learn when
ne
marking is required, when it is optional, and when it is ungrammatical?
Methods:
We conducted two studies using (a) acceptability judgment and (b) elicited production methods with children (aged 4-5, 5-6 and 9-10 years) and adults.
Results:
All age groups showed effects of
statistical preemption
: the greater the frequency with which a particular verb appears with versus without
ne
marking on the subject – relative to other verbs – the greater the extent to which participants (a) accepted and (b) produced
ne
over zero-marked subjects. Both children and adults also showed effects of clause-level semantics, showing greater acceptance of
ne
over zero-marked subjects for intentional than unintentional actions. Some evidence of semantic effects at the level of the verb was observed in the elicited production task for children and the judgment task for adults.
Conclusions:
participants mainly learn ergative marking on an input-based verb-by-verb basis (i.e., via statistical preemption; verb-level semantics), but are also sensitive to clause-level semantic considerations (i.e., the intentionality of the action). These findings add to a growing body of work which suggests that children learn semi-regular, exception-filled systems using both statistics and semantics.
This paper presents a novel space-time feature-based human activity analysis system. We detect Space Time Interest Points (STIP) and generate their description based on the facet model. The proposed ...approach detects interest points in video data using the three-dimensional facet model efficiently. Then we describe each interest point by three-dimensional Haar wavelet transform and time derivatives of different order obtained from said facet model. Here we represent each video clip following the bag-of-words approach by learning feature specific dictionary. Finally, classification is done using non-linear SVM with χ 2 -kernel. We evaluate the performance of our system on standard datasets like Weizmann, KTH, UCF sports, ICD, UCF YouTube, and UCF50 and get better, or at least comparable results compared to other state-of-the-art systems.
The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, ...i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.
How do language learners avoid the production of verb argument structure overgeneralization errors (
*The clown laughed the man
c.f.
The clown made the man laugh
), while retaining the ability to ...apply such generalizations productively when appropriate? This question has long been seen as one that is both particularly central to acquisition research and particularly challenging. Focussing on causative overgeneralization errors of this type, a previous study reported a computational model that learns, on the basis of corpus data and human-derived verb-semantic-feature ratings, to predict adults’ by-verb preferences for less- versus more-transparent causative forms (e.g., *
The clown laughed the man
vs
The clown made the man laugh
) across English, Hebrew, Hindi, Japanese and K’iche Mayan. Here, we tested the ability of this model (and an expanded version with multiple hidden layers) to explain binary grammaticality judgment data from children aged 4;0-5;0, and elicited-production data from children aged 4;0-5;0 and 5;6-6;6 (
N
=48 per language). In general, the model successfully simulated both children’s judgment and production data, with correlations of
r
=0.5-0.6 and
r
=0.75-0.85, respectively, and also generalized to unseen verbs. Importantly, learners of all five languages showed some evidence of making the types of overgeneralization errors – in both judgments and production – previously observed in naturalistic studies of English (e.g.,
*I’m dancing it
). Together with previous findings, the present study demonstrates that a simple learning model can explain (a) adults’ continuous judgment data, (b) children’s binary judgment data and (c) children’s production data (with no training of these datasets), and therefore constitutes a plausible mechanistic account of the acquisition of verbs’ argument structure restrictions.
In this paper, we propose a novel multiple pyramids based image inpainting method using local patch statistics and geometric feature-based sparse representation to maintain texture consistency and ...structure coherence. First, we approximate each patch in the target region (region to be inpainted) by statistically dominant local candidate patches to preserve local consistency. Then each approximated patch is refined by a sparse representation of candidate patches based on local steering kernel (LSK) feature to retain texture quality. We also propose a multiple pyramids based approach to generate several inpainted versions of the input image, one for each of the pyramids. Finally, we combine the inpainted images by gradient-based weighted average to produce the final inpainted image. This approach helps to maintain structure coherence and to remove artifacts which may appear in the inpainted images due to different initial scales of the individual pyramids. The proposed method is tested on a wide range of natural images for scratch and blob/object removal. We have presented both quantitative and qualitative comparison with the existing methods to demonstrate the superiority of the proposed method.
This study presents a novel ensemble classifier-based off-line handwritten word recognition system following a holistic approach. Here each handwritten word is recognised using two handcrafted ...features, namely (i) Arnold transform-based feature that addresses local directional feature which depends on the stroke orientation distribution of cursive word and (ii) oriented curvature-based feature which is basically the histogram of curvelet index and one machine generated feature using deep convolution neural network (DCNN). In this study, a new architecture of DCNN is proposed for handwritten word recognition. These features are recognised by three classifiers separately. Finally, the decision of three classifiers is combined to predict the ultimate word class level. To fuse the decision of individual classifiers, the authors have explored three strategies: (i) vote for strongest decision, (ii) vote for majority decision and (iii) vote for the sum of the decisions. The proposed handwritten word recognition system is tested on three handwritten word databases: (i) CENPARMI database, (ii) IAM database and (iii) ISIHWD database. The performance of the proposed system is promising and comparable to state-of-the-art handwriting recognition systems.
Due to occlusion, lighting condition, variation in clothing dance video classification is a challenging problem in computer vision domain. In this paper we present a local spatiotemporal feature ...model on manifold for Indian Classical Dance (ICD) classification. We represent features at each space-time interest point as a covariance matrix by fusing different order spatial and temporal derivatives. Each video clip is then represented in bag-of-words framework on manifold using Jensen-Bregman LogDet Divergence. Classification is done by popular non-linear SVM with ?2-kernel. We evaluate our system on a ICD dataset created from YouTube and get 69.39% accuracy which is better than that of the state-of-the-art human activity classification algorithms. We have also tested our algorithms on human activity benchmark datasets like KTH, and UCF50 and get promising results compared to the state-of-the-art methods.
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