Table Of Content
- Energy-based model
- A maverick physicist is building a case for scrapping quantum gravity
- 2. Data Generation and Molecular Representation
- Generative molecular design in low data regimes
- Seven from MIT elected to American Academy of Arts and Sciences for 2024
- COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems
In this study, we address this limitation by developing an evolutionary design method. The method employs deep learning models to extract the inherent knowledge from a database of materials and is used to effectively guide the evolutionary design. In the proposed method, the Morgan fingerprint vectors of seed molecules are evolved using the techniques of mutation and crossover within the genetic algorithm. Then, a recurrent neural network is used to reconstruct the final fingerprints into actual molecular structures while maintaining their chemical validity.
Energy-based model
These databases are chemical datasets by combining and screening existing databases not only for generating molecules, but also for the validation of various machine learning methods as the benchmark. The MOSES platform [37] screens the ZINC by some rules and divides the final data set into three groups, training set, test set and scaffold set to ensure the diversity of molecules. There are also task-specific databases, which are used in other tasks related to drug discovery, such as L1000 CMap [38] with gene expression profiles, CEPDB [39] for learning potential structures of photo-voltaics and so on. The last type, chemical space datasets, contains compounds of specific atom composition in a way similar to enumerating chemical space. For instance, quantum machine (QM) [40, 41] extracted from GDB [42, 43], containing molecules composed of CHONF and their quantum chemical properties. Table 1 shows the specific description of the datasets which are commonly used for de novo molecular design, including the number of compounds contained in these datasets up to now, released years, links, etc.
A maverick physicist is building a case for scrapping quantum gravity
Building on SeqGAN [53], called objective-reinforced generative adversarial networks (ORGAN) [51], was proposed where added the expert-based rewards under the framework of a WGAN [52]. The combined rewards from the discriminator and domain-specific objectives were extended to the training process that the generator was trained as an agent (refer Figure 2.2). ORGANIC [54], a promotion of ORGAN for inverse-design chemistry, implemented the molecular biased generation towards specific properties. Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Department of Electrical Engineering and Computer Science (EECS) have developed a model that better selects lead molecule candidates based on desired properties. It also modifies the molecular structure needed to achieve a higher potency, while ensuring the molecule is still chemically valid. Chemists use expert knowledge and conduct manual tweaking of the structure of molecules, adding and subtracting functional groups — groups of atoms and bonds with specific properties.
2. Data Generation and Molecular Representation
This information is used by a readout phase to generate the feature vector for the molecule, which is then used for the property prediction. Closed-loop workflow for computational autonomous molecular design (CAMD) for medical therapeutics. It consists of data generation, feature extraction, predictive machine learning and an inverse molecular design engine.
Generative molecular design in low data regimes
Deep neural networks are divided into discriminative models and generative models. The discriminative models which reflect the difference between heterogeneous data are to find the optimal classification [7]. The generative models, modeling the prior probability, represent the similarity of congener data.
VAE-based models aim at maximizing the evidence lower bound (ELBO) of the likelihood with Kullback-Leibler divergence. Notably, the latent space of VAE for molecular generation is potentially operated such as controlling the specific properties and the training process is stable. However, reconstructing the training sets limits the ability of exploring in unknown chemical space. The past few decades have observed the arising of keen interest in computational methods especially with the emergence of deep learning.
Seven from MIT elected to American Academy of Arts and Sciences for 2024
In the increasing direction, the average S1 increases by approximately 60% for 50,000 training data; however, it increases by merely 45% for 10,000 training data. This seems to suggest that these results reflect the difference in the performance of the RNN models. The RNN model trained with a larger dataset learns more decoding rules and would decode fingerprints into more diverse molecular structures to meet the goals.
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Hence, it is natural to extend such models for de novo molecular design in drug discovery [20]. Different from using discriminative models to screen databases and classify molecules as active or inactive, deep generative models design new molecules with target properties from scratch. The desire for generating molecules automatically has been mentioned in the past by Gómez-Bombarelli et al. [21].
COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems
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QSARs, also known simply as Hansch equations, are a series of equations that relate observable biological effects to specific, measurable properties of molecules. In the increasing direction of S1, the molecules that evolve without any constraints exhibit higher rates of S1 change than those that evolve within the constraints, as shown in Fig. Under the constraints, the maximum LUMO is fixed at 0.0 eV, as depicted in Fig. These constraints are therefore responsible for suppressing the increase in S1. The GA procedure was implemented using the Distributed Evolutionary Algorithms (DEAP) library in Python. The size of the population, crossover rate, and mutation rate are set to 50, 0.7, and 0.3, respectively.
In the first test, the researchers’ model generated 100 percent chemically valid molecules from a sample distribution, compared to SMILES models that generated 43 percent valid molecules from the same distribution. Given a desired property, the model optimizes a lead molecule by using the prediction algorithm to modify its vector — and, therefore, structure — by editing the molecule’s functional groups to achieve a higher potency score. It repeats this step for multiple iterations, until it finds the highest predicted potency score. Then, the model finally decodes a new molecule from the updated vector, with modified structure, by compiling all the corresponding clusters.
Additionally, the generated molecules with the proposed molecular design technique follow trends similar to that of the training data, which evidently demonstrates the learning and data efficiency of the proposed energy-based model trained with QC-assisted learning. Owing to the reliability of the QC-based molecular design framework in terms of accurate property prediction and efficient targeted molecular design, the proposed strategies can be easily adopted in laboratories for experimental validation. The efficacy of the presented QC-based techniques implemented on noisy near-term quantum devices like quantum annealers has further illustrated the promise of QC for the design of novel molecules in the NISQ as well as the fault-tolerant era.
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