Guided diffusion for inverse molecular design Nature Computational Science

Table Of Content

• Optimization strategy for molecular design
• April 22, 2024 UCLA Living Biofoundry provides NEW resources for chemical separation and analysis
• ‘Humanity’s spacecraft’ Voyager 1 is back online and still exploring
• ‘Designer molecules’ could create tailor-made quantum devices
• QC-assisted molecule generation framework
• Reconstruction of lossless molecular representations from fingerprints

This method increases the likelihood of generating more valid and synthetically tractable molecules and sometimes accelerates overall stochastic searches by using in-depth domain knowledge. However, predefined chemical rules and fragment libraries can lead to bias, and therefore the entire optimization process is at risk of converging to local optima. Moreover, every time the application changes, new chemical rules would have to be specified. For some emerging areas, it is challenging to determine a well-established guide for structural changes. However, more diverse and complex assessments are needed to evaluate the candidates precisely but promptly.

Optimization strategy for molecular design

As a result, the deep learning-based functions, d(∙) and f(∙), enable successful molecular evolution by acquiring the knowledge latent in the molecular data. In most cases, multiple evolutions of the same seed molecule occur along different design paths owing to the randomness of GA. Therefore, more diverse offspring can be obtained using an iterative approach. Examples of the molecules that evolved from two seed molecules, in the absence and presence of the constraints, are summarized in Fig.

April 22, 2024 UCLA Living Biofoundry provides NEW resources for chemical separation and analysis

Systems that attempt to automate molecule design have cropped up in recent years, but their problem is validity. Those systems, Jin says, often generate molecules that are invalid under chemical rules, and they fails to produce molecules with optimal properties. Generative model such as 3D-scaffold [69] can be used to inverse design novel candidates with desired target properties starting from core scaffold or functional group. Both scaffold tree structure and molecular graph structure are encoded into their own vectors, where molecules are group together by similarity. Examples of evolved molecules for two seed molecules (A, B) in the absence and presence of constraints. The DFT-simulated and DNN-predicted (in parentheses) energy values are annotated together.

‘Humanity’s spacecraft’ Voyager 1 is back online and still exploring

Highlights and benchmark of predictive ML methods, their comparison, including their key features, advantages, and disadvantages. Molecular representation with all possible formulation used in the literature for predictive and generative modeling. Fifty seed molecules were randomly selected from the chemical library and evolved in both. You can choose from a list of different molecule representations including; ball and stick, stick, van der Waals spheres, wireframe and lines.

We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles. These steps are then repeated in a closed loop, thus improving and optimizing the data representation, property prediction, and new data generation component. Once we have confidence in our workflow to generate valid new molecules, the validation step with DFT can be bypassed or replaced with an ML predictive tool to make the workflow computationally more efficient.

Molecular design with automated quantum computing-based deep learning and optimization npj Computational ... - Nature.com

Molecular design with automated quantum computing-based deep learning and optimization npj Computational ....

Posted: Mon, 14 Aug 2023 07:00:00 GMT [source]

QC-assisted molecule generation framework

In recent years, de novo molecular design, a concept of generating molecules with desired from scratch, can be implemented by either professional experts or machines. Due to the development of generative models, molecular generation not only decreases the searching space of chemical molecules but also time consumption for drug discovery compared with humans. Here, we overview some typical molecular generative models based on two classical representations in the following and summary the timeline of them in Figure 3. All of the generative models discussed above generate molecules in the form of 2D graphs or SMILES strings. Models to generate molecules directly in the form of 3D coordinates have also recently gained attention [57,108,109].

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Reconstruction of lossless molecular representations from fingerprints

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.

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.

Building on this for a drug discovery application, we recently proposed a model [69] to generate 3D coordinates of molecules while always preserving the desired scaffolds, as depicted in Figure 5. This approach has generated synthesizable drug-like molecules that show a high docking score against the target protein. Other scaffold-based models to generate molecules in the form of 2D graphs/SMILES strings are also published in the literature [110,111,112,113,114]. Expert-engineered molecular representations have been extensively used for predictive modeling in the last decade, which includes properties of the molecules [41,42], structured text sequences [43,44,45] (SMILES, InChI), molecular fingerprints [46], among others. Such representations are carefully selected for each specific problem using domain expertise, a lot of resources, and time.

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].

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.

These latent representations can be further used to perform molecular property estimation tasks by passing them as input to a separate feedforward network. For a molecular generation, we employ an iterative optimization procedure that utilizes a quantum annealer to solve formulated quadratic unconstrained binary optimization (QUBO) problems. 1c, a surrogate model is constructed to estimate the free energy of the molecule–property pair with the trained conditional energy-based model. After formulating a QUBO problem that integrates the linear surrogate model with structural constraints, the problem is then solved using a quantum annealer to generate potential molecular candidates.

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.

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.

Evolutionary techniques like genetic algorithms19 and discrete combinatorial optimization approaches like mixed-integer programming20 have demonstrated their utility for the design of various molecules. However, genetic algorithms require manual adjustment of heuristic rules for different optimization problems and do not guarantee optimality, while combinatorial optimization approaches may exhibit difficulty in solving large-scale nonlinear optimization problems21. These computational challenges can be tackled by deep learning methods that utilize sophisticated neural network architectures for constructing generative models for molecular design.