A system and method for optimizing the latent space in generative machine learning models, and applications of the optimizations for use in the de novo generation of molecules for both ligand-based and pocket-based generation. The ligand-based optimizations comprise a tunable reward system based on a multi-property model and further define new measurable metrics: molecular novelty and uniqueness. The pocket-based optimizations comprise an initial multi-property optimization followed up by either a seed-based optimization or a relaxed-based optimization.

The present disclosure relates to inhibitors of mitochondrial function. Methods of screening compounds for mitochondrial inhibition are disclosed. Also described are methods of using mitochondrial inhibitors called mitoriboscins—mitochondrial-based therapeutic compounds having anti-cancer and antibiotic properties—to prevent or treat cancer, bacterial infections, and pathogenic yeast, as well as methods of using mitochondrial inhibitors to provide anti-aging benefits. Specific mitoriboscin compounds and groups of mitoriboscins are also disclosed.

Methods described herein include receiving data from flowing a plurality of aptamers over a sample of tumor cells randomly affixed to a surface of a microfluidic device. The tumor cells may include one or more unknown tumor subtypes of cells. The plurality of aptamers may include a plurality of aptamer families. Each aptamer family of the plurality of aptamer families may be determined to bind to at least one possible subtype of the tumor cells. The data may include a measure of binding affinity of each aptamer family to the tumor cells. The method may include analyzing the measure of the binding affinity of each aptamer family to the tumor cells. The analyzing may include classifying the binding affinity. The method may also include determining one or more aptamer families that characterize the one or more unknown tumor subtypes of cells based on the classifying.

An electronic device generates, according to a connection graph structure corresponding to a reference drug molecule, an atomic latent vector corresponding to the reference drug molecule. The device performs atom masking processing on the atomic latent vector to obtain a scaffold latent vector and a sidechain latent vector included in the atomic latent vector. The device generates a target scaffold latent vector with a target transition degree between the scaffold latent vector and the target scaffold latent vector according to a spatial distribution of the scaffold latent vector. The device generates a transitioned drug molecule according to the target scaffold latent vector and the sidechain latent vector.

An electronic device generates, according to a connection graph structure corresponding to a reference drug molecule, an atomic latent vector corresponding to the reference drug molecule. The device performs atom masking processing on the atomic latent vector to obtain a scaffold latent vector and a sidechain latent vector included in the atomic latent vector. The device generates a target scaffold latent vector with a target transition degree between the scaffold latent vector and the target scaffold latent vector according to a spatial distribution of the scaffold latent vector. The device generates a transitioned drug molecule according to the target scaffold latent vector and the sidechain latent vector.

The present disclosure relates to systems, methods, and products for identifying candidate molecule. The system includes a non-transitory memory storing instructions; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to receive drug data; convert the drug data into at least one point in a latent space using a grammar variational auto-encoder (VAE) model; receive a query for the at least one candidate molecule; select one or more points in the latent space; and create a k-dimensional tree graph based on the query for the at least one candidate molecule and the selected one or more points; determine a plurality of paths according to an interpolation technique; receive preference data; determine an optimum path; determine at least one candidate point on the optimum path; and determine a drug molecular structure using an inverse of the grammar VAE model.

The present invention concerns screening methods to identify compounds that regulate activity of RSH enzymes such as Rel, and specifically Rel synthetase and/or Rel hydrolase activity. Also intended are compounds that interact and regulate Rel synthetase and/or hydrolase activity. These compounds are valuable to target persister cells not affected by traditional antibiotics.

The present invention concerns screening methods to identify compounds that regulate activity of RSH enzymes such as Rel, and specifically Rel synthetase and/or Rel hydrolase activity. Also intended are compounds that interact and regulate Rel synthetase and/or hydrolase activity. These compounds are valuable to target persister cells not affected by traditional antibiotics.

In a method of molecular scaffold hopping an interface of a scheduler computer sends instructions, prepared by the scheduler computer, to a job runner computer to perform a plurality of separate computational tasks. Each of the separate computational tasks includes calculating one or more chemical properties for a query molecule or molecules in a library of molecules. One or more of the plurality of separate computational tasks performed on the job runner computer are preemptible computing instances. Status indicators sent from the job runner computer are received by the interface for each of the plurality of separate computational tasks. The indicators are one of: incomplete, completed, or failed computing instances. The interface resends the instructions to the job runner computer that correspond to the separate computational tasks having the failed computing instance indicator to increase fault-tolerance against the separate computational tasks not attaining the completed computing instance indicator.

A computer-implemented method for estimating a state-action value function for a Fitted Q-iteration is provided including obtaining a set of tuples D and a discount factor γ, each of the set of tuples including a state s, an action a, a reward r, and a resulting state s′, constructing a zero-suppressed decision diagram (ZDD) of feature vectors {ϕ(s′, a′)|a′∈(s′)} for each of the resulting states s′ of the set of tuples, where the feature vector ϕ(s, a) is a sparse bit vector {0,1}.sup.D and (s′) is the set of actions applicable at state s′, updating parameters w∈.sup.D, θ of a state-action value function Q (s, a; w, θ); and repeating the updating step a predetermined times by incrementing t.