International Business Machines Corporation

Agency for Science, Technology and Research

Kahini Wadhawan

Payel Das

Enara C Vijil

Inkit Padhi

James L Hedrick

Yi Yan Yang

Diptikalyan Saha

Vijay Arya

Pin-Yu Chen

Pang Kern Jeremy Tan

Cicero Nogueira Dos Santos

Sebastian Gehrmann

Samuel Chung Hoffman

Devika Sondhi

Flaviu Cipcigan

Tom D J Sercu

A query-based generic end-to-end molecular optimization ('QMO') system framework, method and computer program product for optimizing molecules, such as for accelerating drug discovery. The QMO framework decouples representation learning and guided search and applies to any plug-in encoder-decoder with continuous latent representations. QMO framework directly incorporates evaluations based on chemical modeling, analysis packages, and pre-trained machine-learned prediction models for efficient molecule optimization using a query-based guided search method based on zeroth order optimization. The QMO features efficient guided search with molecular property evaluations and constraints obtained using the predictive models and chemical modeling and analysis packages. QMO tasks include optimizing drug-likeness and penalized log P scores with similarity constraints and improving the target binding affinity of existing drugs to pathogens such as the SARS-CoV-2 main protease protein while preserving the desired drug properties. QMO tasks further improves optimizing antimicrobial peptides toward lower toxicity.

Query-based molecule optimization and applications to functional molecule discovery

A processor can receive input data to a computer code and output data that the computer code produces corresponding to the input data. Based on the input data and the output data, the processor can build a decision tree that links input conditions to the output data. Using the input conditions, the processor can run a condition generator model created by fine-tuning a pre-trained programming language model, where the condition generator model predicts next condition that is likely to occur in the computer code. Based on the next condition, the processor can generate input instances and run the computer code using the generated input instances. Based on output instances output by the computer code based on the generated input instances, the processor can detect at least one flow path in the computer code, which had not been previously built in the decision tree.

Test case generation

A computer-implemented method according to one aspect includes training a latent variable model (LVM), utilizing labeled data and unlabeled data within a data set; training a classifier, utilizing the labeled data and associated labels within the data set; and generating new data having a predetermined set of labels, utilizing the trained LVM and the trained classifier.

Generating attribute-based samples

De novo, artificial intelligence (AI) designed antimicrobial peptides (AMPs), antibacterial products comprising the AMPs and methods for treating bacterial infections using the products are provided. In one or more embodiments, the AMPs were designed using conditional latent attribute space sampling (CLaSS). The AMPs comprise up to twenty natural amino acids in length, including one with twelve and another with thirteen natural amino acids in length. The AMPs demonstrate low-toxicity and show high antimicrobial potency against diverse pathogens including multi-medication-resistant Gram negative

Artificial intelligence designed antimicrobial peptides

Growing community of inventors

Ferozepur, India

Kahini Wadhawan