Editorial 1 : Can parties be de-recognised or de-registered?
Context
The Election Commission of India (ECI) in its report on enforcement of Model Code of Conduct (MCC) has stated that it expects star campaigners to lead by example and not vitiate the fabric of society. This has raised a debate about ECI powers to rein in MCC violations.
What are registered parties?
- Section 29A of the Representation of the People Act, 1951 (RP Act) lays down the requirements for registration of a political party with the ECI.
- Any political party that seeks registration should submit a copy of its memorandum/constitution.
- Such document should declare that the party shall bear true faith and allegiance to the Constitution of India.
- It should also bear allegiance to the principles of socialism, secularism and democracy, and uphold the sovereignty, unity and integrity of India.
- Registered political parties enjoy the following legal benefits – (a) tax exemption for donations received under Section 13A of the Income Tax Act, 1961, (b) common symbol for contesting general elections to the Lok Sabha/State Assemblies, and (c) twenty ‘star campaigners’ during election campaign.
- As per the ECI, there are 2,790 active registered political parties in India.
What are recognised parties?
- A registered party is referred to as a Registered Unrecognised Political Party (RUPP).
- Political parties are recognised as a ‘national’ or ‘State’ party under the provisions of The Election Symbols (Reservation and Allotment) Order, 1968 (Symbols Order) by the ECI.
- The criteria for recognition at the ‘national’ or ‘State’ level consists of winning requisite number of seats and/or obtaining required percentage of votes in a general election to Lok Sabha or State Assembly.
- At present, there are six ‘national’ parties, and sixty-one ‘State’ parties that have been recognised. These recognised parties enjoy additional concessions of having a reserved symbol during elections and forty ‘star campaigners’.
The issues
- It has been noticed that less than a third of RUPPs contest elections. The RP Act does not confer explicit powers on the ECI to de-register any political party if it fails to contest elections, conduct inner-party elections or lodge requisite returns. The
- Supreme Court in Indian National Congress versus Institute of Social Welfare & Ors (2002) had held that the ECI does not have power to de-register any political party under the RP Act.
- It may de-register only under exceptional circumstances like registration being obtained by fraud or the political party ceasing to have allegiance to the Constitution or if it is declared unlawful by the Government.
- The RUPPs that don’t contest elections raise concerns over the possible misuse of income tax exemption and donations collected being used for money laundering.
- The MCC prohibits using caste and communal feelings to secure votes, and bribing or intimidation of voters.
- Recognised political parties are guilty of violating the MCC on various occasions. However, it has been observed that the ECI on such occasions at best bars leaders from campaigning for a short period of two to three days.
Suggestions
- The ECI in its memorandum for electoral reforms (2016) has suggested amendment to the law that would empower the ECI to deregister a party.
- The Law Commission in its 255th report (2015) on ‘Electoral reforms’ has also recommended amendments for de-registration of a political party if it fails to contest elections for 10 consecutive years.
- These recommendations should be implemented.
- Under Paragraph 16A of the Symbols order, the ECI has the power to suspend or withdraw recognition of a recognised political party for its failure to observe MCC or follow lawful directions of the Commission.
Conclusion
It has probably been used only once for three weeks in 2015 when the recognition of National People’s Party was suspended for failure to follow the directions of the ECI. Strict action under this provision would have a salutary effect in ensuring adherence to the MCC.
Editorial 2 : The use of AI in drug development
Introduction
Drug development is an expensive and time-consuming process. However, the advent of Artificial Intelligence (AI) has opened up a world of possibilities with respect to fast-tracking drug development.
The process
- The process of developing a drug starts with identifying and validating a target. A target is a biological molecule (usually a gene or a protein) to which a drug directly binds in order to work.
- The overwhelming majority of targets are proteins. Only those proteins with ideal sites where drugs can go and dock to do their business are druggable proteins.
- Target proteins are identified in the discovery phase, wherein a target protein sequence is fed into a computer which looks for the best-fitting drug out of millions in the library of small molecules for which the structures are stored in the computer.
- The process assumes that the structures of the target protein and drug are known. If not, the computer uses models to understand the sites where a drug can bind.
- This discovery process avoids time-consuming laboratory experiments that require expensive chemicals and reagents and have a high failure rate.
- Once the suitable protein target and its drug are identified, the research moves to the pre-clinical phase, where the potential drug candidates are tested outside a biological system, using cells and animals for the drug’s safety and toxicity.
- After this, as part of the clinical phase, the drug is tested on a small number of human patients before being used on more patients for efficacy and safety.
- Finally, the drug undergoes regulatory approval and marketing and post-market survey phases. Due to a high failure rate, the discovery phase limits the number of drugs that pass and carry on to the pre-clinical and clinical phases.
How can AI help this process?
- AI has the potential to revolutionise target discovery and understand drug-target interaction by drastically cutting down time, increasing the accuracy of prediction of interaction between a drug and its target, and saving money.
- The development of two AI-based prediction tools, AlphaFold and RoseTTAFold, developed by researchers at DeepMind, a Google company, and the University of Washington, U.S., respectively, has provided a major scientific breakthrough in the last four years in the area of computational drug development.
- Both tools are based on deep neural networks. The tools’ neural networks use massive amounts of input data to produce the desired output — the three-dimensional structures of proteins.
- Published recently, the new avatars of AlphaFold and RoseTTAFold, called AlphaFold 3 and RoseTTAFold All-Atom, respectively, take the capability of these tools to an entirely new level.
- The significant difference between the upgraded versions and their previous forms is their capability to predict not just static structures of proteins and protein-protein interactions but also their ability to predict structures and interactions for any combination of protein, DNA, and RNA, including modifications, small molecules and ions.
- Additionally, the new versions use generative diffusion-based architectures (one kind of AI model) to predict structural complexes.
The Drawbacks
- With all the promise and potential in drug development, AI tools have provide up to 80% accuracy in predicting interactions.
- Second, the tools can only aid a single phase of drug development, target discovery and drug-target interaction. It will still have to go through the pre-clinical and clinical development phases, and there is no guarantee that the AI-derived molecules will result in success in those phases.
- Third, one of the challenges with diffusion-based architecture is model hallucinations, where insufficient training data causes the tool to produce incorrect or non-existent predictions.
- Finally, unlike the previous versions of AlphaFold, DeepMind has not released the code for AlphaFold 3, restricting its independent verification, broad utilisation and use for protein-small molecule interaction studies.
Way forward
- Developing new AI tools for drug development requires large-scale computing infrastructure, especially ones with fast Graphics Processing Units (GPUs) to run multiple tasks with longer sequences.
- GPU chips are expensive, and with newer and faster ones being produced by hardware makers every year, they have a quick expiration date.
- India needs such large-scale computing infrastructure. That, along with a lack of skilled AI scientists, unlike in the U.S. and China, is the second reason why researchers in India could not establish a first-mover advantage in developing
- However, with a growing number of pharmaceutical organisations, India can lead the way in applying AI tools in target discovery, identification, and drug testing.